Dynamic Programming
0 likes3,165 views
Dynamic Programming : Dynamic programming is a technique for solving problems by breaking them down into smaller subproblems, solving each subproblem once, and storing the solution to each subproblem so that it can be reused in the future. Some characteristics of dynamic programming include: Optimal substructure: Dynamic programming problems typically have an optimal substructure, meaning that the optimal solution to the problem can be obtained by solving the subproblems optimally and combining their solutions. Overlapping subproblems: Dynamic programming problems often involve overlapping subproblems, meaning that the same subproblems are solved multiple times. To avoid solving the same subproblem multiple times, dynamic programming algorithms store the solutions to the subproblems in a table or array, so that they can be reused later. Bottom-up approach: Dynamic programming algorithms usually solve problems using a bottom-up approach, meaning that they start by solving the smallest subproblems and work their way up to the larger ones. Efficiency: Dynamic programming algorithms can be very efficient, especially when the subproblems overlap significantly. By storing the solutions to the subproblems and reusing them, dynamic programming algorithms can avoid redundant computations and achieve good time and space complexity. Applicability: Dynamic programming is applicable to a wide range of problems, including optimization problems, decision problems, and problems that involve sequential decisions. It is often used to solve problems in computer science, operations research, and economics. Algorithm Design Techniques Iterative techniques, Divide and Conquer, Dynamic Programming, Greedy Algorithms.
1 of 9
Downloaded 15 times
Ad
Recommended
Dynamic programming
Dynamic programmingYıldırım Tam A. Plannig
The document mentions that in dynamic programming, "Programming" refers to "planning", not computer programming.
Dynamic programming
Dynamic programmingMelaku Bayih Demessie Dynamic Programming is a method for solving a complex problem by breaking it down into a collection of simpler subproblems, solving each of those subproblems just once, and storing their solutions using a memory-based data structure (array, map,etc).
Dijkstra's Algorithm 
Dijkstra's Algorithm Rashik Ishrak Nahian One of the main reasons for the popularity of Dijkstra's Algorithm is that it is one of the most important and useful algorithms available for generating (exact) optimal solutions to a large class of shortest path problems. The point being that this class of problems is extremely important theoretically, practically, as well as educationally.
CPU Scheduling in OS Presentation
CPU Scheduling in OS Presentationusmankiyani1 CPU scheduling allows processes to share the CPU by pausing execution of some processes to allow others to run. The scheduler selects which process in memory runs on the CPU. There are four types of scheduling decisions: when a process pauses for I/O, switches from running to ready, finishes I/O, or terminates. Scheduling can be preemptive, where a higher priority process interrupts a running one, or non-preemptive. Common algorithms are first come first serve, shortest job first, priority, and round robin. Real-time scheduling aims to process data without delays and ensures the highest priority tasks run first.
Rabin Karp ppt
Rabin Karp pptshreyasBharadwaj15 The Rabin-Karp algorithm is a string-searching algorithm that uses hashing to find patterns in strings.
The Rabin-Karp algorithm makes use of hash functions and the rolling hash technique.
organisational development ppt
organisational development pptkohlisudeep18 This document summarizes various aspects of organizational development (OD) efforts. It discusses that OD is a planned change approach that aims to improve organizational effectiveness and employee well-being through interventions at the task, structure, technology, or people levels. It is a long-term and ongoing process that relies on experiential learning and uses action research. Various OD interventions are outlined, including sensitivity training, team building, survey feedback, and process consultation. The key aspects and processes of different interventions like team building, survey feedback, and process consultation are also summarized.
Human Computer Interaction Chapter 3 HCI in the Software Process and Design ...
Human Computer Interaction Chapter 3 HCI in the Software Process and Design ...VijiPriya Jeyamani The document discusses various aspects of the software development life cycle and human-computer interaction design rules. It describes the typical phases of software development like requirements specification, architectural design, detailed design, coding, testing, and maintenance. It also discusses design principles, standards, guidelines, heuristics and patterns that can improve usability. Iterative design and prototyping approaches are presented as methods to refine requirements and designs. The importance of documenting design rationale is highlighted.
Oriental foods
Oriental foodsPALANIANANTH.S Oriental fermented foods are traditionally made foods from Asia that use fermentation to preserve and enhance nutrients. Key fermented foods discussed include natto from Japan made from fermented soybeans, minchin from wheat fermented for weeks, piden which are preserved eggs from China, and poi from Hawaii made by mashing and pounding taro. Fermentation involves microbes like bacteria and molds transforming the ingredients through processes like lactic acid production. These foods are significant sources of nutrition and have distinct flavors, textures, and importance in various Asian cuisines.
Greedy algorithms
Greedy algorithmssandeep54552 Greedy algorithms work by making locally optimal choices at each step to arrive at a global optimal solution. They require that the problem exhibits the greedy choice property and optimal substructure. Examples that can be solved with greedy algorithms include fractional knapsack problem, minimum spanning tree, and activity selection. The fractional knapsack problem is solved greedily by sorting items by value/weight ratio and filling the knapsack completely. The 0/1 knapsack problem differs in that items are indivisible.
Dynamic pgmming
Dynamic pgmmingDr. C.V. Suresh Babu This document provides an overview of dynamic programming. It begins by explaining that dynamic programming is a technique for solving optimization problems by breaking them down into overlapping subproblems and storing the results of solved subproblems in a table to avoid recomputing them. It then provides examples of problems that can be solved using dynamic programming, including Fibonacci numbers, binomial coefficients, shortest paths, and optimal binary search trees. The key aspects of dynamic programming algorithms, including defining subproblems and combining their solutions, are also outlined.
Exhaustive Search
Exhaustive SearchKasun Ranga Wijeweera Exhaustive search involves systematically enumerating all potential solutions to a problem and checking if each one is a valid solution. It can solve problems that efficient algorithms cannot by simply trying every possibility, though this becomes intractable for large problems. The traveling salesman problem is an example where checking the length of every possible tour route between cities requires exhaustive search for even moderate numbers of cities. While slow, exhaustive search guarantees finding the optimal solution if allowed to run to completion.
Greedy Algorihm
Greedy AlgorihmMuhammad Amjad Rana This document outlines greedy algorithms, their characteristics, and examples of their use. Greedy algorithms make locally optimal choices at each step in the hopes of finding a global optimum. They are simple to implement and fast, but may not always reach the true optimal solution. Examples discussed include coin changing, traveling salesman, minimum spanning trees using Kruskal's and Prim's algorithms, and Huffman coding.
Dynamic programming
Dynamic programmingShakil Ahmed The document discusses dynamic programming and provides examples of problems that can be solved using dynamic programming including unidirectional traveling salesman problem, coin change, longest common subsequence, and longest increasing subsequence. Source code is presented for solving these problems using dynamic programming including dynamic programming tables, tracing optimal solutions, and time complexity analysis. Various online judges are listed that contain sample problems relating to these dynamic programming techniques.
Travelling Salesman Problem
Travelling Salesman ProblemDaniel Raditya The document discusses the Travelling Salesman Problem (TSP), which aims to find the shortest route to visit each city in a list exactly once and return to the origin city. It describes TSP as an NP-hard problem, belonging to the complexity class NP-complete. The document provides background on TSP, explaining it cannot be solved in polynomial time using techniques like linear programming. While an efficient solution to the general TSP has not been found, there are approximation algorithms that provide near-optimal solutions.
The analysis synthesis model of compilation
The analysis synthesis model of compilationHuawei Technologies The document describes the analysis-synthesis model of compilation which has two parts: analysis breaks down the source program into pieces and creates an intermediate representation, and synthesis constructs the target program from the intermediate representation. During analysis, the operations of the source program are determined and recorded in a syntax tree where each node represents an operation and children are the arguments.
Introduction to Dynamic Programming, Principle of Optimality
Introduction to Dynamic Programming, Principle of OptimalityBhavin Darji Introduction
Dynamic Programming
How Dynamic Programming reduces computation
Steps in Dynamic Programming
Dynamic Programming Properties
Principle of Optimality
Problem solving using Dynamic Programming
Introduction to dynamic programming
Introduction to dynamic programmingAmisha Narsingani Dynamic programming is an algorithm design technique for optimization problems that reduces time by increasing space usage. It works by breaking problems down into overlapping subproblems and storing the solutions to subproblems, rather than recomputing them, to build up the optimal solution. The key aspects are identifying the optimal substructure of problems and handling overlapping subproblems in a bottom-up manner using tables. Examples that can be solved with dynamic programming include the knapsack problem, shortest paths, and matrix chain multiplication.
Divide and conquer - Quick sort
Divide and conquer - Quick sortMadhu Bala divide and conquer , Example , benefits, Quick sort algorithm, complexity of quick sort, merits of quick sort
Introduction TO Finite Automata
Introduction TO Finite AutomataRatnakar Mikkili This document provides an introduction to finite automata. It defines key concepts like alphabets, strings, languages, and finite state machines. It also describes the different types of automata, specifically deterministic finite automata (DFAs) and nondeterministic finite automata (NFAs). DFAs have a single transition between states for each input, while NFAs can have multiple transitions. NFAs are generally easier to construct than DFAs. The next class will focus on deterministic finite automata in more detail.
Dynamic programming class 16
Dynamic programming class 16Kumar Dynamic programming is an algorithm design technique that solves problems by breaking them down into smaller overlapping subproblems and storing the results of already solved subproblems, rather than recomputing them. It is applicable to problems exhibiting optimal substructure and overlapping subproblems. The key steps are to define the optimal substructure, recursively define the optimal solution value, compute values bottom-up, and optionally reconstruct the optimal solution. Common examples that can be solved with dynamic programming include knapsack, shortest paths, matrix chain multiplication, and longest common subsequence.
Algorithm chapter 10
Algorithm chapter 10chidabdu This document discusses P, NP and NP-complete problems. It begins by introducing tractable and intractable problems, and defines problems that can be solved in polynomial time as tractable, while problems that cannot are intractable. It then discusses the classes P and NP, with P containing problems that can be solved deterministically in polynomial time, and NP containing problems that can be solved non-deterministically in polynomial time. The document concludes by defining NP-complete problems as those in NP that are as hard as any other problem in the class, in that any NP problem can be reduced to an NP-complete problem in polynomial time.
Daa:Dynamic Programing
Daa:Dynamic Programingrupali_2bonde Dynamic programming is used to solve optimization problems by breaking them down into subproblems. It solves each subproblem only once, storing the results in a table to lookup when the subproblem recurs. This avoids recomputing solutions and reduces computation. The key is determining the optimal substructure of problems. It involves characterizing optimal solutions recursively, computing values in a bottom-up table, and tracing back the optimal solution. An example is the 0/1 knapsack problem to maximize profit fitting items in a knapsack of limited capacity.
Randomized algorithms ver 1.0
Randomized algorithms ver 1.0Dr. C.V. Suresh Babu This document discusses randomized algorithms. It begins by listing different categories of algorithms, including randomized algorithms. Randomized algorithms introduce randomness into the algorithm to avoid worst-case behavior and find efficient approximate solutions. Quicksort is presented as an example randomized algorithm, where randomness improves its average runtime from quadratic to linear. The document also discusses the randomized closest pair algorithm and a randomized algorithm for primality testing. Both introduce randomness to improve efficiency compared to deterministic algorithms for the same problems.
Greedy Algorithm
Greedy AlgorithmWaqar Akram This document provides an introduction to greedy algorithms. It defines greedy algorithms as algorithms that make locally optimal choices at each step in the hope of finding a global optimum. The document then provides examples of problems that can be solved using greedy algorithms, including counting money, scheduling jobs, finding minimum spanning trees, and the traveling salesman problem. It also provides pseudocode for a general greedy algorithm and discusses some properties of greedy algorithms.
Greedy algorithm
Greedy algorithmInternational Islamic University The document discusses greedy algorithms and their application to optimization problems. It provides examples of problems that can be solved using greedy approaches, such as fractional knapsack and making change. However, it notes that some problems like 0-1 knapsack and shortest paths on multi-stage graphs cannot be solved optimally with greedy algorithms. The document also describes various greedy algorithms for minimum spanning trees, single-source shortest paths, and fractional knapsack problems.
Tsp branch and-bound
Tsp branch and-boundSaravanan Natarajan The document discusses the Travelling Salesman Problem (TSP). TSP aims to find the shortest possible route for a salesman to visit each city in a list only once and return to the origin city. It describes the problem as finding the optimal or least cost Hamiltonian circuit in a graph where cities are nodes and distances between cities are edge costs. The document provides an example problem with 5 cities, calculates possible routes and costs, and illustrates the branch and bound algorithm to solve TSP by systematically eliminating suboptimal routes until the optimal route is found.
What Is Dynamic Programming? | Dynamic Programming Explained | Programming Fo...
What Is Dynamic Programming? | Dynamic Programming Explained | Programming Fo...Simplilearn This presentation on 'What Is Dynamic Programming?' will acquaint you with a clear understanding of how this programming paradigm works with the help of a real-life example. In this Dynamic Programming Tutorial, you will understand why recursion is not compatible and how you can solve the problems involved in recursion using DP. Finally, we will cover the dynamic programming implementation of the Fibonacci series program. So, let's get started!
The topics covered in this presentation are:
1. Introduction
2. Real-Life Example of Dynamic Programming
3. Introduction to Dynamic Programming
4. Dynamic Programming Interpretation of Fibonacci Series Program
5. How Does Dynamic Programming Work?
What Is Dynamic Programming?
In computer science, something is said to be efficient if it is quick and uses minimal memory. By storing the solutions to subproblems, we can quickly look them up if the same problem arises again. Because there is no need to recompute the solution, this saves a significant amount of calculation time. But hold on! Efficiency comprises both time and space difficulty. But, why does it matter if we reduce the time required to solve the problem only to increase the space required? This is why it is critical to realize that the ultimate goal of Dynamic Programming is to obtain considerably quicker calculation time at the price of a minor increase in space utilized. Dynamic programming is defined as an algorithmic paradigm that solves a given complex problem by breaking it into several sub-problems and storing the results of those sub-problems to avoid the computation of the same sub-problem over and over again.
What is Programming?
Programming is an act of designing, developing, deploying an executlable software solution to the given user-defined problem.
Programming involves the following stages.
- Problem Statement
- Algorithms and Flowcharts
- Coding the program
- Debug the program.
- Documention
- Maintainence
Simplilearn’s Python Training Course is an all-inclusive program that will introduce you to the Python development language and expose you to the essentials of object-oriented programming, web development with Django and game development. Python has surpassed Java as the top language used to introduce U.S.
Learn more at: https://www.simplilearn.com/mobile-and-software-development/python-development-training
Greedy Algorithm - Knapsack Problem
Greedy Algorithm - Knapsack ProblemMadhu Bala The document discusses the knapsack problem and greedy algorithms. It defines the knapsack problem as an optimization problem where given constraints and an objective function, the goal is to find the feasible solution that maximizes or minimizes the objective. It describes the knapsack problem has having two versions: 0-1 where items are indivisible, and fractional where items can be divided. The fractional knapsack problem can be solved using a greedy approach by sorting items by value to weight ratio and filling the knapsack accordingly until full.
Dynamic Programming | Economics
Dynamic Programming | EconomicsTransweb Global Inc Dynamic programming is a technique used to solve complex problems by breaking them down into smaller, simpler modules. It involves comparing and combining solutions from previous analyses to determine the optimal solution. While similar to greedy algorithms, dynamic programming is better as it does not rely on local optima. Dynamic programming finds applications in fields like bioinformatics for problems involving sequence alignment, protein folding, and RNA structure prediction.
Ad
More Related Content
What's hot (20)
Greedy algorithms
Greedy algorithmssandeep54552 Greedy algorithms work by making locally optimal choices at each step to arrive at a global optimal solution. They require that the problem exhibits the greedy choice property and optimal substructure. Examples that can be solved with greedy algorithms include fractional knapsack problem, minimum spanning tree, and activity selection. The fractional knapsack problem is solved greedily by sorting items by value/weight ratio and filling the knapsack completely. The 0/1 knapsack problem differs in that items are indivisible.
Dynamic pgmming
Dynamic pgmmingDr. C.V. Suresh Babu This document provides an overview of dynamic programming. It begins by explaining that dynamic programming is a technique for solving optimization problems by breaking them down into overlapping subproblems and storing the results of solved subproblems in a table to avoid recomputing them. It then provides examples of problems that can be solved using dynamic programming, including Fibonacci numbers, binomial coefficients, shortest paths, and optimal binary search trees. The key aspects of dynamic programming algorithms, including defining subproblems and combining their solutions, are also outlined.
Exhaustive Search
Exhaustive SearchKasun Ranga Wijeweera Exhaustive search involves systematically enumerating all potential solutions to a problem and checking if each one is a valid solution. It can solve problems that efficient algorithms cannot by simply trying every possibility, though this becomes intractable for large problems. The traveling salesman problem is an example where checking the length of every possible tour route between cities requires exhaustive search for even moderate numbers of cities. While slow, exhaustive search guarantees finding the optimal solution if allowed to run to completion.
Greedy Algorihm
Greedy AlgorihmMuhammad Amjad Rana This document outlines greedy algorithms, their characteristics, and examples of their use. Greedy algorithms make locally optimal choices at each step in the hopes of finding a global optimum. They are simple to implement and fast, but may not always reach the true optimal solution. Examples discussed include coin changing, traveling salesman, minimum spanning trees using Kruskal's and Prim's algorithms, and Huffman coding.
Dynamic programming
Dynamic programmingShakil Ahmed The document discusses dynamic programming and provides examples of problems that can be solved using dynamic programming including unidirectional traveling salesman problem, coin change, longest common subsequence, and longest increasing subsequence. Source code is presented for solving these problems using dynamic programming including dynamic programming tables, tracing optimal solutions, and time complexity analysis. Various online judges are listed that contain sample problems relating to these dynamic programming techniques.
Travelling Salesman Problem
Travelling Salesman ProblemDaniel Raditya The document discusses the Travelling Salesman Problem (TSP), which aims to find the shortest route to visit each city in a list exactly once and return to the origin city. It describes TSP as an NP-hard problem, belonging to the complexity class NP-complete. The document provides background on TSP, explaining it cannot be solved in polynomial time using techniques like linear programming. While an efficient solution to the general TSP has not been found, there are approximation algorithms that provide near-optimal solutions.
The analysis synthesis model of compilation
The analysis synthesis model of compilationHuawei Technologies The document describes the analysis-synthesis model of compilation which has two parts: analysis breaks down the source program into pieces and creates an intermediate representation, and synthesis constructs the target program from the intermediate representation. During analysis, the operations of the source program are determined and recorded in a syntax tree where each node represents an operation and children are the arguments.
Introduction to Dynamic Programming, Principle of Optimality
Introduction to Dynamic Programming, Principle of OptimalityBhavin Darji Introduction
Dynamic Programming
How Dynamic Programming reduces computation
Steps in Dynamic Programming
Dynamic Programming Properties
Principle of Optimality
Problem solving using Dynamic Programming
Introduction to dynamic programming
Introduction to dynamic programmingAmisha Narsingani Dynamic programming is an algorithm design technique for optimization problems that reduces time by increasing space usage. It works by breaking problems down into overlapping subproblems and storing the solutions to subproblems, rather than recomputing them, to build up the optimal solution. The key aspects are identifying the optimal substructure of problems and handling overlapping subproblems in a bottom-up manner using tables. Examples that can be solved with dynamic programming include the knapsack problem, shortest paths, and matrix chain multiplication.
Divide and conquer - Quick sort
Divide and conquer - Quick sortMadhu Bala divide and conquer , Example , benefits, Quick sort algorithm, complexity of quick sort, merits of quick sort
Introduction TO Finite Automata
Introduction TO Finite AutomataRatnakar Mikkili This document provides an introduction to finite automata. It defines key concepts like alphabets, strings, languages, and finite state machines. It also describes the different types of automata, specifically deterministic finite automata (DFAs) and nondeterministic finite automata (NFAs). DFAs have a single transition between states for each input, while NFAs can have multiple transitions. NFAs are generally easier to construct than DFAs. The next class will focus on deterministic finite automata in more detail.
Dynamic programming class 16
Dynamic programming class 16Kumar Dynamic programming is an algorithm design technique that solves problems by breaking them down into smaller overlapping subproblems and storing the results of already solved subproblems, rather than recomputing them. It is applicable to problems exhibiting optimal substructure and overlapping subproblems. The key steps are to define the optimal substructure, recursively define the optimal solution value, compute values bottom-up, and optionally reconstruct the optimal solution. Common examples that can be solved with dynamic programming include knapsack, shortest paths, matrix chain multiplication, and longest common subsequence.
Algorithm chapter 10
Algorithm chapter 10chidabdu This document discusses P, NP and NP-complete problems. It begins by introducing tractable and intractable problems, and defines problems that can be solved in polynomial time as tractable, while problems that cannot are intractable. It then discusses the classes P and NP, with P containing problems that can be solved deterministically in polynomial time, and NP containing problems that can be solved non-deterministically in polynomial time. The document concludes by defining NP-complete problems as those in NP that are as hard as any other problem in the class, in that any NP problem can be reduced to an NP-complete problem in polynomial time.
Daa:Dynamic Programing
Daa:Dynamic Programingrupali_2bonde Dynamic programming is used to solve optimization problems by breaking them down into subproblems. It solves each subproblem only once, storing the results in a table to lookup when the subproblem recurs. This avoids recomputing solutions and reduces computation. The key is determining the optimal substructure of problems. It involves characterizing optimal solutions recursively, computing values in a bottom-up table, and tracing back the optimal solution. An example is the 0/1 knapsack problem to maximize profit fitting items in a knapsack of limited capacity.
Randomized algorithms ver 1.0
Randomized algorithms ver 1.0Dr. C.V. Suresh Babu This document discusses randomized algorithms. It begins by listing different categories of algorithms, including randomized algorithms. Randomized algorithms introduce randomness into the algorithm to avoid worst-case behavior and find efficient approximate solutions. Quicksort is presented as an example randomized algorithm, where randomness improves its average runtime from quadratic to linear. The document also discusses the randomized closest pair algorithm and a randomized algorithm for primality testing. Both introduce randomness to improve efficiency compared to deterministic algorithms for the same problems.
Greedy Algorithm
Greedy AlgorithmWaqar Akram This document provides an introduction to greedy algorithms. It defines greedy algorithms as algorithms that make locally optimal choices at each step in the hope of finding a global optimum. The document then provides examples of problems that can be solved using greedy algorithms, including counting money, scheduling jobs, finding minimum spanning trees, and the traveling salesman problem. It also provides pseudocode for a general greedy algorithm and discusses some properties of greedy algorithms.
Greedy algorithm
Greedy algorithmInternational Islamic University The document discusses greedy algorithms and their application to optimization problems. It provides examples of problems that can be solved using greedy approaches, such as fractional knapsack and making change. However, it notes that some problems like 0-1 knapsack and shortest paths on multi-stage graphs cannot be solved optimally with greedy algorithms. The document also describes various greedy algorithms for minimum spanning trees, single-source shortest paths, and fractional knapsack problems.
Tsp branch and-bound
Tsp branch and-boundSaravanan Natarajan The document discusses the Travelling Salesman Problem (TSP). TSP aims to find the shortest possible route for a salesman to visit each city in a list only once and return to the origin city. It describes the problem as finding the optimal or least cost Hamiltonian circuit in a graph where cities are nodes and distances between cities are edge costs. The document provides an example problem with 5 cities, calculates possible routes and costs, and illustrates the branch and bound algorithm to solve TSP by systematically eliminating suboptimal routes until the optimal route is found.
What Is Dynamic Programming? | Dynamic Programming Explained | Programming Fo...
What Is Dynamic Programming? | Dynamic Programming Explained | Programming Fo...Simplilearn This presentation on 'What Is Dynamic Programming?' will acquaint you with a clear understanding of how this programming paradigm works with the help of a real-life example. In this Dynamic Programming Tutorial, you will understand why recursion is not compatible and how you can solve the problems involved in recursion using DP. Finally, we will cover the dynamic programming implementation of the Fibonacci series program. So, let's get started!
The topics covered in this presentation are:
1. Introduction
2. Real-Life Example of Dynamic Programming
3. Introduction to Dynamic Programming
4. Dynamic Programming Interpretation of Fibonacci Series Program
5. How Does Dynamic Programming Work?
What Is Dynamic Programming?
In computer science, something is said to be efficient if it is quick and uses minimal memory. By storing the solutions to subproblems, we can quickly look them up if the same problem arises again. Because there is no need to recompute the solution, this saves a significant amount of calculation time. But hold on! Efficiency comprises both time and space difficulty. But, why does it matter if we reduce the time required to solve the problem only to increase the space required? This is why it is critical to realize that the ultimate goal of Dynamic Programming is to obtain considerably quicker calculation time at the price of a minor increase in space utilized. Dynamic programming is defined as an algorithmic paradigm that solves a given complex problem by breaking it into several sub-problems and storing the results of those sub-problems to avoid the computation of the same sub-problem over and over again.
What is Programming?
Programming is an act of designing, developing, deploying an executlable software solution to the given user-defined problem.
Programming involves the following stages.
- Problem Statement
- Algorithms and Flowcharts
- Coding the program
- Debug the program.
- Documention
- Maintainence
Simplilearn’s Python Training Course is an all-inclusive program that will introduce you to the Python development language and expose you to the essentials of object-oriented programming, web development with Django and game development. Python has surpassed Java as the top language used to introduce U.S.
Learn more at: https://www.simplilearn.com/mobile-and-software-development/python-development-training
Greedy Algorithm - Knapsack Problem
Greedy Algorithm - Knapsack ProblemMadhu Bala The document discusses the knapsack problem and greedy algorithms. It defines the knapsack problem as an optimization problem where given constraints and an objective function, the goal is to find the feasible solution that maximizes or minimizes the objective. It describes the knapsack problem has having two versions: 0-1 where items are indivisible, and fractional where items can be divided. The fractional knapsack problem can be solved using a greedy approach by sorting items by value to weight ratio and filling the knapsack accordingly until full.
Similar to Dynamic Programming (20)
Dynamic Programming | Economics
Dynamic Programming | EconomicsTransweb Global Inc Dynamic programming is a technique used to solve complex problems by breaking them down into smaller, simpler modules. It involves comparing and combining solutions from previous analyses to determine the optimal solution. While similar to greedy algorithms, dynamic programming is better as it does not rely on local optima. Dynamic programming finds applications in fields like bioinformatics for problems involving sequence alignment, protein folding, and RNA structure prediction.
Use of Symbolic Computation is Solving Structural Analysis Problems
Use of Symbolic Computation is Solving Structural Analysis ProblemsJaved Alam Presentation is about the use symbolic computation and its use in solving structural analysis problems
YSU Quest10 Presentation
How to Design an Algorithm
How to Design an AlgorithmAfaq Mansoor Khan An algorithm is a plan, a logical step-by-step process for solving a problem. Algorithms are normally written as a flowchart or in pseudo-code.
A flowchart is a diagram that represents a set of instructions. Flowcharts normally use standard symbols to represent the different types of instructions. These symbols are used to construct the flowchart and show the step-by-step solution to the problem.
Algorithms Design Patterns
Algorithms Design PatternsAshwin Shiv The document discusses various algorithms design approaches and patterns including divide and conquer, greedy algorithms, dynamic programming, backtracking, and branch and bound. It provides examples of each along with pseudocode. Specific algorithms discussed include binary search, merge sort, knapsack problem, shortest path problems, and the traveling salesman problem. The document is authored by Ashwin Shiv, a second year computer science student at NIT Delhi.
251 - Alogarithms Lects.pdf
251 - Alogarithms Lects.pdfAbdulkadir Jibril The document summarizes a lecture on algorithms and algorithm design from a course on algorithms. It introduces the concept of algorithms and what will be covered in the course, which includes analyzing algorithms, sorting techniques as a case study, and various algorithmic problems and solutions. It also discusses implementation issues and keeping mathematical analysis independent of specific programming languages or machines. As an example, it analyzes the brute force algorithm for solving the 2D maxima problem in pseudo-code and calculates its running time.
Our presentation on algorithm design
Our presentation on algorithm designNahid Hasan The document discusses algorithm design. It defines an algorithm as a step-by-step solution to a mathematical or computer problem. Algorithm design is the process of creating such mathematical solutions. The document outlines several approaches to algorithm design, including greedy algorithms, divide and conquer, dynamic programming, and backtracking. It also discusses graph algorithms, flowcharts, and the importance of algorithm design in solving complex problems efficiently.
Operation Research Techniques 
Operation Research Techniques Lijin Mathew This document presents 15 quantitative techniques and tools: Linear Programming, Queuing Theory, Inventory Control Method, Net Work Analysis, Replacement Problems, Sequencing, Integer Programming, Assignment Problems, Transportation Problems, Decision Theory and Game Theory, Markov Analysis, Simulation, Dynamic Programming, Goal Programming, and Symbolic Logic. It provides a brief overview of each technique, describing its purpose and typical applications.
Project report 
Project report Vikram Singh This document is a project report for a "Vehicles Parking Management System" submitted by two students for their bachelor's degree. It includes sections on acknowledging those who supported the project, an introduction describing the purpose of managing vehicle parking records, definitions of the problem the system aims to solve, objectives, scope, system requirements, design processes, modules, and how to run the project. The report provides an overview of the key aspects of the parking management system project.
Maxima presentation
Maxima presentationMohammad Tawfik This document discusses using the open-source Maxima software to perform structural analysis computations symbolically and numerically. It presents Maxima's capabilities for symbolic differentiation, integration, matrix operations, and solving differential equations, which are useful for structural analysis. Six representative structural analysis problems are solved using Maxima to demonstrate its ease of use. The conclusion is that Maxima can efficiently handle symbolic and numeric solutions to structural analysis problems, providing a free alternative to commercial software packages for teaching purposes.
CH-1.1 Introduction (1).pptx
CH-1.1 Introduction (1).pptxsatvikkushwaha1 This document provides an introduction to the analysis of algorithms. It discusses algorithm specification, performance analysis frameworks, and asymptotic notations used to analyze algorithms. Key aspects covered include time complexity, space complexity, worst-case analysis, and average-case analysis. Common algorithms like sorting and searching are also mentioned. The document outlines algorithm design techniques such as greedy methods, divide and conquer, and dynamic programming. It distinguishes between recursive and non-recursive algorithms and provides examples of complexity analysis for non-recursive algorithms.
Algorithm Design
Algorithm DesignMD.ASHIQUZZAMAN KHONDAKER The document presents an algorithm design presentation given by 5 students. It introduces algorithms and algorithm design, explaining that algorithms are procedures for solving problems and algorithm design is the process of creating mathematical processes to solve problems. It discusses the importance of algorithm design and techniques for designing algorithms, including dynamic programming, graph algorithms, divide and conquer, backtracking, greedy algorithms, and flowcharts. The presentation concludes by listing some advantages of designing algorithms such as ease of use, performance, scalability, and stability.
Dynamic programming prasintation eaisy
Dynamic programming prasintation eaisyahmed51236 Dynamic programming is a technique for solving complex problems by breaking them down into simpler sub-problems. It involves storing solutions to sub-problems for later use, avoiding recomputing them. Examples where it can be applied include matrix chain multiplication and calculating Fibonacci numbers. For matrix chains, dynamic programming finds the optimal order for multiplying matrices with minimum computations. For Fibonacci numbers, it calculates values in linear time by storing previous solutions rather than exponentially recomputing them through recursion.
DP Project Report
DP Project ReportChawal Ukesh This document surveys and compares several software options for solving dynamic programming problems: LINDO, TORA, and MATLAB. LINDO and TORA are specifically designed for optimization problems like linear programming and can handle a variety of dynamic programming problems. The document provides examples of assigning teachers to courses and solving a transportation problem using LINDO and TORA. Both software packages find the optimal solutions. The document also discusses limitations of the different software. MATLAB is a numerical computing environment that can also solve some dynamic programming problems but was not demonstrated with an example.
Notion of an algorithm
Notion of an algorithmNisha Soms The document discusses the importance of algorithms and their role in problem solving. It defines what an algorithm is and explains that they are sets of instructions to solve problems efficiently. The document outlines different algorithm design techniques and how algorithms shape applications like search engines, recommendations, and maps. It also discusses qualities of good algorithms like correctness, termination, and performance and analyzing algorithms through pseudocode and empirical testing.
The Download: Tech Talks by the HPCC Systems Community, Episode 16
The Download: Tech Talks by the HPCC Systems Community, Episode 16HPCC Systems This episode will feature our 2018 HPCC Systems summer interns:
Shah Muhammad Hamdi, PhD student, CS at Georgia State University - Dimensionality Reduction and Feature Selection in ECL-ML
Hamdi will discuss the parallel implementation of Principal Component Analysis (PCA) using the Parallel Block Basic Linear Algebra Subsystem (PBblas) library and ECL implementations of feature selection algorithms for the HPCC Systems platform.
Robert Kennedy, PhD student in Computer Science at Florida Atlantic University - Parallel Distributed Deep Learning on HPCC Systems
Robert will cover what he implemented during his summer internship. Combining HPCC Systems and Google’s TensorFlow, Robert created a parallel stochastic gradient descent algorithm to provide a basis for future deep neural network research and to enhance HPCC System’s distributed neural network training capabilities.
Aramis Tanelus, programmer and senior at American Heritage High School where he is the lead programmer for the Advanced Robotics Team - Developing HPCC Systems Data Ingestion APIs for Common Robotic Sensors.
Aramis’s project will make it easy for anyone in robotics around the world to ingest data from common robotic sensors into an HPCC Systems platform for use in data analysis. Aramis will be speaking about his work on the autonomous agricultural robot and implementing new packages for the Robotics Operating System to interface with HPCC Systems for big data analysis.
Saminda Wijeratne, Masters student, Computational Science and Engineering at Georgia Institute of Technology, Atlanta - MPI Proof of Concept
The built-in "Message Passing" library in HPCC Systems is designed to handle these communications among dissimilar components and perform non-trivial communication patterns among them. Saminda will explore how this library currently operates and how we can introduce a different implementation such as an existing popular library called MPI.
Overview of Approximation and Randomized Algorithms KARAN 461.pptx
Overview of Approximation and Randomized Algorithms KARAN 461.pptxkaranjogi2021 Randomized algorithms are computational methods that make random choices during execution to achieve improved performance, simplicity, or probability guarantees. Unlike deterministic algorithms, which follow a strict sequence of operations, randomized algorithms use random numbers or coin flips to guide their decisions. This incorporation of randomness allows them to handle complex problems more efficiently, particularly in cases where deterministic solutions are too slow or difficult to design.
Key Concepts
At the heart of a randomized algorithm is the use of a random source, often a pseudo-random number generator. The algorithm's behavior can vary from one run to another even on the same input. However, it is designed in such a way that, on average or with high probability, it produces the correct or near-optimal solution.
Randomized algorithms fall into two primary categories:
Las Vegas Algorithms: These always produce the correct result, but their runtime may vary depending on the random choices made. The randomness only affects efficiency, not correctness. An example is Randomized QuickSort, where the pivot is chosen randomly to avoid worst-case scenarios.
Monte Carlo Algorithms: These may produce an incorrect result with a small probability, but they generally run faster or are simpler than deterministic counterparts. An example is the Miller-Rabin primality test, which determines whether a number is prime with high probability.
Divide and conquer algorithm
Divide and conquer algorithmCHANDAN KUMAR This is a very important type of algorithm paradigm which is mostly used to solve any kind of problems like sorting ( merge sort, quick sort), binary search, Tower of Hanoi, etc.
PROBLEM SOLVING TECHNIQUES
PROBLEM SOLVING TECHNIQUESsudhanagarajan5 The document provides an overview of problem solving techniques in computer science. It discusses:
- Algorithms and programs as solutions to problems expressed in programming languages.
- Key aspects of problem solving including problem definition, getting started, working backwards, and utilizing past experiences.
- General problem solving strategies such as "divide and conquer" and dynamic programming.
- Algorithm design techniques like top-down design and choosing appropriate data structures.
- Implementing algorithms through modularization, variable naming, documentation, debugging, and testing.
- Verifying algorithms through input/output assertions, symbolic execution, and proving program segments.
- Analyzing algorithm efficiency by reducing redundant computations, early termination detection, and trading storage for
Ad
More from Bharat Bhushan (20)
NIMCET 2023 : Questions
NIMCET 2023 : QuestionsBharat Bhushan This document contains a test examination with multiple choice questions on mathematics, English, computer science, and reasoning. There are 52 questions in total covering topics like trigonometry, sets, relations, sequences, coding/decoding, direction sense, data interpretation from tables, and basic computer concepts. The test is for a batch of NIMCET conducted in March 2022 by Buddha Science and Technical Institute located in Kokar, Ranchi.
Heuristics Search Techniques in AI 
Heuristics Search Techniques in AI Bharat Bhushan What is Heuristics?
A heuristic is a technique that is used to solve a problem faster than the classic methods. These techniques are used to find the approximate solution of a problem when classical methods do not. Heuristics are said to be the problem-solving techniques that result in practical and quick solutions.
Heuristics are strategies that are derived from past experience with similar problems. Heuristics use practical methods and shortcuts used to produce the solutions that may or may not be optimal, but those solutions are sufficient in a given limited timeframe.
History
Psychologists Daniel Kahneman and Amos Tversky have developed the study of Heuristics in human decision-making in the 1970s and 1980s. However, this concept was first introduced by the Nobel Laureate Herbert A. Simon, whose primary object of research was problem-solving.
Why do we need heuristics?
Heuristics are used in situations in which there is the requirement of a short-term solution. On facing complex situations with limited resources and time, Heuristics can help the companies to make quick decisions by shortcuts and approximated calculations. Most of the heuristic methods involve mental shortcuts to make decisions on past experiences.
Heuristic techniques
The heuristic method might not always provide us the finest solution, but it is assured that it helps us find a good solution in a reasonable time.
Based on context, there can be different heuristic methods that correlate with the problem's scope. The most common heuristic methods are - trial and error, guesswork, the process of elimination, historical data analysis. These methods involve simply available information that is not particular to the problem but is most appropriate. They can include representative, affect, and availability heuristics.
We can perform the Heuristic techniques into two categories:
Direct Heuristic Search techniques in AI
It includes Blind Search, Uninformed Search, and Blind control strategy. These search techniques are not always possible as they require much memory and time. These techniques search the complete space for a solution and use the arbitrary ordering of operations.
The examples of Direct Heuristic search techniques include Breadth-First Search (BFS) and Depth First Search (DFS).
Weak Heuristic Search techniques in AI
It includes Informed Search, Heuristic Search, and Heuristic control strategy. These techniques are helpful when they are applied properly to the right types of tasks. They usually require domain-specific information.
The examples of Weak Heuristic search techniques include Best First Search (BFS) and A*.
Heuristics Search Techniques
Heuristics Search TechniquesBharat Bhushan What is Heuristics?
A heuristic is a technique that is used to solve a problem faster than the classic methods. These techniques are used to find the approximate solution of a problem when classical methods do not. Heuristics are said to be the problem-solving techniques that result in practical and quick solutions.
Heuristics are strategies that are derived from past experience with similar problems. Heuristics use practical methods and shortcuts used to produce the solutions that may or may not be optimal, but those solutions are sufficient in a given limited timeframe.
History
Psychologists Daniel Kahneman and Amos Tversky have developed the study of Heuristics in human decision-making in the 1970s and 1980s. However, this concept was first introduced by the Nobel Laureate Herbert A. Simon, whose primary object of research was problem-solving.
Why do we need heuristics?
Heuristics are used in situations in which there is the requirement of a short-term solution. On facing complex situations with limited resources and time, Heuristics can help the companies to make quick decisions by shortcuts and approximated calculations. Most of the heuristic methods involve mental shortcuts to make decisions on past experiences.
Heuristic techniques
The heuristic method might not always provide us the finest solution, but it is assured that it helps us find a good solution in a reasonable time.
Based on context, there can be different heuristic methods that correlate with the problem's scope. The most common heuristic methods are - trial and error, guesswork, the process of elimination, historical data analysis. These methods involve simply available information that is not particular to the problem but is most appropriate. They can include representative, affect, and availability heuristics.
We can perform the Heuristic techniques into two categories:
Direct Heuristic Search techniques in AI
It includes Blind Search, Uninformed Search, and Blind control strategy. These search techniques are not always possible as they require much memory and time. These techniques search the complete space for a solution and use the arbitrary ordering of operations.
The examples of Direct Heuristic search techniques include Breadth-First Search (BFS) and Depth First Search (DFS).
Weak Heuristic Search techniques in AI
It includes Informed Search, Heuristic Search, and Heuristic control strategy. These techniques are helpful when they are applied properly to the right types of tasks. They usually require domain-specific information.
The examples of Weak Heuristic search techniques include Best First Search (BFS) and A*.
Hill Climbing Algorithm in Artificial Intelligence
Hill Climbing Algorithm in Artificial IntelligenceBharat Bhushan Hill Climbing Algorithm in Artificial Intelligence
Hill climbing algorithm is a local search algorithm which continuously moves in the direction of increasing elevation/value to find the peak of the mountain or best solution to the problem. It terminates when it reaches a peak value where no neighbor has a higher value.
Hill climbing algorithm is a technique which is used for optimizing the mathematical problems. One of the widely discussed examples of Hill climbing algorithm is Traveling-salesman Problem in which we need to minimize the distance traveled by the salesman.
It is also called greedy local search as it only looks to its good immediate neighbor state and not beyond that.
A node of hill climbing algorithm has two components which are state and value.
Hill Climbing is mostly used when a good heuristic is available.
In this algorithm, we don't need to maintain and handle the search tree or graph as it only keeps a single current state.
Features of Hill Climbing:
Following are some main features of Hill Climbing Algorithm:
Generate and Test variant: Hill Climbing is the variant of Generate and Test method. The Generate and Test method produce feedback which helps to decide which direction to move in the search space.
Greedy approach: Hill-climbing algorithm search moves in the direction which optimizes the cost.
No backtracking: It does not backtrack the search space, as it does not remember the previous states.
State-space Diagram for Hill Climbing:
The state-space landscape is a graphical representation of the hill-climbing algorithm which is showing a graph between various states of algorithm and Objective function/Cost.
On Y-axis we have taken the function which can be an objective function or cost function, and state-space on the x-axis. If the function on Y-axis is cost then, the goal of search is to find the global minimum and local minimum. If the function of Y-axis is Objective function, then the goal of the search is to find the global maximum and local maximum.
Hill Climbing Algorithm in Artificial Intelligence
Hill Climbing Algorithm in Artificial IntelligenceBharat Bhushan Hill Climbing Algorithm in Artificial Intelligence
Hill climbing algorithm is a local search algorithm which continuously moves in the direction of increasing elevation/value to find the peak of the mountain or best solution to the problem. It terminates when it reaches a peak value where no neighbor has a higher value.
Hill climbing algorithm is a technique which is used for optimizing the mathematical problems. One of the widely discussed examples of Hill climbing algorithm is Traveling-salesman Problem in which we need to minimize the distance traveled by the salesman.
It is also called greedy local search as it only looks to its good immediate neighbor state and not beyond that.
A node of hill climbing algorithm has two components which are state and value.
Hill Climbing is mostly used when a good heuristic is available.
In this algorithm, we don't need to maintain and handle the search tree or graph as it only keeps a single current state.
Features of Hill Climbing:
Following are some main features of Hill Climbing Algorithm:
Generate and Test variant: Hill Climbing is the variant of Generate and Test method. The Generate and Test method produce feedback which helps to decide which direction to move in the search space.
Greedy approach: Hill-climbing algorithm search moves in the direction which optimizes the cost.
No backtracking: It does not backtrack the search space, as it does not remember the previous states.
State-space Diagram for Hill Climbing:
The state-space landscape is a graphical representation of the hill-climbing algorithm which is showing a graph between various states of algorithm and Objective function/Cost.
On Y-axis we have taken the function which can be an objective function or cost function, and state-space on the x-axis. If the function on Y-axis is cost then, the goal of search is to find the global minimum and local minimum. If the function of Y-axis is Objective function, then the goal of the search is to find the global maximum and local maximum.
Control Strategies in AI 
Control Strategies in AI Bharat Bhushan Control Strategies
Control Strategy in Artificial Intelligence
scenario is a technique or strategy, tells us about which rule has to be applied next while searching for the solution of a problem within problem space.
It helps us to decide which rule has to apply next without getting stuck at any point.
Characteristics of Control Strategies
A good Control strategy has two main
characteristics:
Control Strategy should cause Motion
Control strategy should be Systematic
Co ntrol Strategy should cause Motion
Each rule or strategy applied should cause the motion because if there will be no motion than such control strategy will never lead to a solution. Motion states about the change of state and if a state will not change then there be no movement from an initial state and we would never solve the problem.
Co ntrol Strategy should be Systematic
Though the strategy applied should create the
motion but if do not follow some systematic
strategy than we are likely to reach the same state
number of times before reaching the solution
which increases the number of steps. Taking care of only first strategy we may go through particular useless sequences of operators several times. Control Strategy should be systematic implies a need for global motion as well as for local motion.
Control Strategies in AI 
Control Strategies in AI Bharat Bhushan Control Strategies
Control Strategy in Artificial Intelligence
scenario is a technique or strategy, tells us about which rule has to be applied next while searching for the solution of a problem within problem space.
It helps us to decide which rule has to apply next without getting stuck at any point.
Characteristics of Control Strategies
A good Control strategy has two main
characteristics:
Control Strategy should cause Motion
Control strategy should be Systematic
Co ntrol Strategy should cause Motion
Each rule or strategy applied should cause the motion because if there will be no motion than such control strategy will never lead to a solution. Motion states about the change of state and if a state will not change then there be no movement from an initial state and we would never solve the problem.
Co ntrol Strategy should be Systematic
Though the strategy applied should create the
motion but if do not follow some systematic
strategy than we are likely to reach the same state
number of times before reaching the solution
which increases the number of steps. Taking care of only first strategy we may go through particular useless sequences of operators several times. Control Strategy should be systematic implies a need for global motion as well as for local motion.
Algorithm Design Technique 
Algorithm Design Technique Bharat Bhushan Dynamic Programming :
Dynamic programming is a technique for solving problems by breaking them down into smaller subproblems, solving each subproblem once, and storing the solution to each subproblem so that it can be reused in the future. Some characteristics of dynamic programming include:
Optimal substructure: Dynamic programming problems typically have an optimal substructure, meaning that the optimal solution to the problem can be obtained by solving the subproblems optimally and combining their solutions.
Overlapping subproblems: Dynamic programming problems often involve overlapping subproblems, meaning that the same subproblems are solved multiple times. To avoid solving the same subproblem multiple times, dynamic programming algorithms store the solutions to the subproblems in a table or array, so that they can be reused later.
Bottom-up approach: Dynamic programming algorithms usually solve problems using a bottom-up approach, meaning that they start by solving the smallest subproblems and work their way up to the larger ones.
Efficiency: Dynamic programming algorithms can be very efficient, especially when the subproblems overlap significantly. By storing the solutions to the subproblems and reusing them, dynamic programming algorithms can avoid redundant computations and achieve good time and space complexity.
Applicability: Dynamic programming is applicable to a wide range of problems, including optimization problems, decision problems, and problems that involve sequential decisions. It is often used to solve problems in computer science, operations research, and economics.
Algorithm Design Techniques
Iterative techniques, Divide and Conquer, Dynamic Programming, Greedy Algorithms.
Production System in AI
Production System in AIBharat Bhushan Problem Characteristics in Artificial Intelligence,
Unit -2 Problem Solving and Searching Techniques
o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
Production System in AI
Production System in AIBharat Bhushan Production System in Artificial Intelligence (AI)
A production system in AI helps create AI-based computer programs. With the help of it, the automation of various types of machines has become an easy task. The types of machines can be a computer, mobile applications, manufacturing tools, or more. The set of rules in a production system in Artificial Intelligence defines the behavior of the machine. It helps the machine respond to the surroundings.
A production system in AI is a type of cognitive architecture that defines specific actions as per certain rules. The rules represent the declarative knowledge of a machine to respond according to different conditions. Today, many expert systems and automation methodologies rely on the rules of production systems.
Global Database
A global database consists of the architecture used as a central data structure. A database contains all the necessary data and information required for the successful completion of a task. It can be divided into two parts as permanent and temporary. The permanent part of the database consists of fixed actions, whereas the temporary part alters according to circumstances.
Learn more about Artificial Neural networks in this insightful Artificial Intelligence Training now!
Production Rules
Production rules in AI are the set of rules that operates on the data fetched from the global database. Also, these production rules are bound with precondition and postcondition that gets checked by the database. If a condition is passed through a production rule and gets satisfied by the global database, then the rule is successfully applied. The rules are of the form A®B, where the right-hand side represents an outcome corresponding to the problem state represented by the left-hand side.
Control System
The control system checks the applicability of a rule. It helps decide which rule should be applied and terminates the process when the system gives the correct output. It also resolves the conflict of multiple conditions arriving at the same time. The strategy of the control system specifies the sequence of rules that compares the condition from the global database to reach the correct result.
Simplicity
The production rule in AI is in the form of an ‘IF-THEN’ statement. Every rule in the production system has a unique structure. It helps represent knowledge and reasoning in the simplest way possible to solve real-world problems. Also, it helps improve the readability and understanding of the production rules.
Problem Characteristics in Artificial Intelligence
Problem Characteristics in Artificial IntelligenceBharat Bhushan Problem Characteristics in Artificial Intelligence,
Unit -2 Problem Solving and Searching Techniques
o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
Problem Characteristics in Artificial Intelligence
Problem Characteristics in Artificial IntelligenceBharat Bhushan Artificial Intelligence is a “way of making a computer, a computer-controlled robot, or software think intelligently, in the similar manner the intelligent humans think”.
Since artificial intelligence (AI) is mainly related to the search process, it is important to have some methodology to choose the best possible solution.
To choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
Introduction to Artificial Intteligence
Introduction to Artificial IntteligenceBharat Bhushan ARTIFICIAL INTELLIGENCE : Introduction ,
Introduction to Artificial Intelligence, Background and Applications, Turing Test and Rational
Agent approaches to AI, Introduction to Intelligent Agents, their structure, behavior and
environment.
2. Problem Solving and Searching Techniques
Problem Characteristics, Production Systems, Control Strategies, Breadth First Search, Depth First
Search, Hill climbing and its Variations, Heuristics Search Techniques: Best First Search, A*
algorithm, Constraint Satisfaction Problem, Means-End Analysis, Introduction to Game Playing,
Min-Max and Alpha-Beta pruning algorithms.
3. Knowledge Representation
Introduction to First Order Predicate Logic, Resolution Principle, Unification, Semantic Nets,
Conceptual Dependencies, Frames, and Scripts, Production Rules, Conceptual Graphs.
Programming in Logic (PROLOG)
4. Dealing with Uncertainty and Inconsistencies
Maintenance System, Default Reasoning, Probabilistic Reasoning, Bayesian Probabilistic
Inference, Possible World Representations.
5. Understanding Natural Languages
Parsing Techniques, Context-Free and
Transformational Grammars, Recursive and Augmented Transition Nets.
Introduction to Artificial Intteligence
Introduction to Artificial IntteligenceBharat Bhushan ARTIFICIAL INTELLIGENCE : Introduction ,
Introduction to Artificial Intelligence, Background and Applications, Turing Test and Rational
Agent approaches to AI, Introduction to Intelligent Agents, their structure, behavior and
environment.
2. Problem Solving and Searching Techniques
Problem Characteristics, Production Systems, Control Strategies, Breadth First Search, Depth First
Search, Hill climbing and its Variations, Heuristics Search Techniques: Best First Search, A*
algorithm, Constraint Satisfaction Problem, Means-End Analysis, Introduction to Game Playing,
Min-Max and Alpha-Beta pruning algorithms.
3. Knowledge Representation
Introduction to First Order Predicate Logic, Resolution Principle, Unification, Semantic Nets,
Conceptual Dependencies, Frames, and Scripts, Production Rules, Conceptual Graphs.
Programming in Logic (PROLOG)
4. Dealing with Uncertainty and Inconsistencies
Maintenance System, Default Reasoning, Probabilistic Reasoning, Bayesian Probabilistic
Inference, Possible World Representations.
5. Understanding Natural Languages
Parsing Techniques, Context-Free and
Transformational Grammars, Recursive and Augmented Transition Nets.
DESIGN AND ANALYSIS OF ALGORITHMS by Bharat Sir 
DESIGN AND ANALYSIS OF ALGORITHMS by Bharat Sir Bharat Bhushan DESIGN AND ANALYSIS OF ALGORITHMS
1. Introduction
Basic Design and Analysis techniques of Algorithms, Correctness of Algorithm.
2. Algorithm Design Techniques
Iterative techniques, Divide and Conquer, Dynamic Programming, Greedy Algorithms.
3. Sorting and Searching Techniques
Elementary sorting techniques–Bubble Sort, Insertion Sort, Merge Sort, Advanced Sorting
techniques - Heap Sort, Quick Sort, Sorting in Linear Time - Bucket Sort, Radix Sort and Count
Sort, Searching Techniques, Medians & Order Statistics, complexity analysis;
4. Lower Bounding Techniques
Decision Trees
5. Balanced Trees
Red-Black Trees
6. Advanced Analysis Technique
Amortized analysis
7. Graphs
Graph Algorithms–Breadth First Search, Depth First Search and its Applications, Minimum
Spanning Trees.
DESIGN AND ANALYSIS OF ALGORITHMS
DESIGN AND ANALYSIS OF ALGORITHMSBharat Bhushan This document outlines the units of a course on design and analysis of algorithms taught by Bharat Bhushan. The course covers basic design techniques like iteration, divide and conquer, and dynamic programming. It also covers sorting algorithms like bubble sort, merge sort, and quick sort. Additionally, the course addresses searching techniques, lower bounding techniques, advanced analysis like amortized analysis, and graph algorithms like breadth first search and minimum spanning trees. The overall document provides an overview of the topics and units to be covered in the algorithms course.
ARTIFICIAL INTELLIGENCE 
ARTIFICIAL INTELLIGENCE Bharat Bhushan ARTIFICIAL INTELLIGENCE
1. Introduction
Introduction to Artificial Intelligence, Background and Applications, Turing Test and Rational
Agent approaches to AI, Introduction to Intelligent Agents, their structure, behavior and environment.
2. Problem Solving and Searching Techniques
Problem Characteristics, Production Systems, Control Strategies, Breadth First Search, Depth First
Search, Hill climbing and its Variations, Heuristics Search Techniques: Best First Search, A*
algorithm, Constraint Satisfaction Problem, Means-End Analysis, Introduction to Game Playing,
Min-Max and Alpha-Beta pruning algorithms.
3. Knowledge Representation
Introduction to First Order Predicate Logic, Resolution Principle, Unification, Semantic Nets,
Conceptual Dependencies, Frames, and Scripts, Production Rules, Conceptual Graphs.
Programming in Logic (PROLOG)
4. Dealing with Uncertainty and Inconsistencies
Maintenance System, Default Reasoning, Probabilistic Reasoning, Bayesian Probabilistic
Inference, Possible World Representations.
5. Understanding Natural Languages
Parsing Techniques, Context-Free and
Transformational Grammars, Recursive and Augmented Transition Nets.
ARTIFICIAL INTELLIGENCE SYLLABUS 
ARTIFICIAL INTELLIGENCE SYLLABUS Bharat Bhushan ARTIFICIAL INTELLIGENCE
1. Introduction
Introduction to Artificial Intelligence, Background and Applications, Turing Test and Rational
Agent approaches to AI, Introduction to Intelligent Agents, their structure, behavior and environment.
2. Problem Solving and Searching Techniques
Problem Characteristics, Production Systems, Control Strategies, Breadth First Search, Depth First
Search, Hill climbing and its Variations, Heuristics Search Techniques: Best First Search, A*
algorithm, Constraint Satisfaction Problem, Means-End Analysis, Introduction to Game Playing,
Min-Max and Alpha-Beta pruning algorithms.
3. Knowledge Representation
Introduction to First Order Predicate Logic, Resolution Principle, Unification, Semantic Nets,
Conceptual Dependencies, Frames, and Scripts, Production Rules, Conceptual Graphs.
Programming in Logic (PROLOG)
4. Dealing with Uncertainty and Inconsistencies
Maintenance System, Default Reasoning, Probabilistic Reasoning, Bayesian Probabilistic
Inference, Possible World Representations.
5. Understanding Natural Languages
Parsing Techniques, Context-Free and
Transformational Grammars, Recursive and Augmented Transition Nets.
Cyber Security 
Cyber Security Bharat Bhushan Presentation on Cyber Security by Bharat Bhushan, Assistant Professor, Ram Lakhan Singh Yadav College, Kokar, Ranchi.
Cyber Security 
Cyber Security Bharat Bhushan Presentation on Cyber Security by Bharat Bhushan, Assistant Professor, Ram Lakhan Singh Yadav College, Kokar, Ranchi.
Ad
Recently uploaded (20)
Studying Drama: Definition, types and elements
Studying Drama: Definition, types and elementsAbdelFattahAdel2 Studying Drama: Definition, types and elements
Understanding P–N Junction Semiconductors: A Beginner’s Guide
Understanding P–N Junction Semiconductors: A Beginner’s GuideGS Virdi Dive into the fundamentals of P–N junctions, the heart of every diode and semiconductor device. In this concise presentation, Dr. G.S. Virdi (Former Chief Scientist, CSIR-CEERI Pilani) covers:
What Is a P–N Junction? Learn how P-type and N-type materials join to create a diode.
Depletion Region & Biasing: See how forward and reverse bias shape the voltage–current behavior.
V–I Characteristics: Understand the curve that defines diode operation.
Real-World Uses: Discover common applications in rectifiers, signal clipping, and more.
Ideal for electronics students, hobbyists, and engineers seeking a clear, practical introduction to P–N junction semiconductors.
World war-1(Causes & impacts at a glance) PPT by Simanchala Sarab(BABed,sem-4...
World war-1(Causes & impacts at a glance) PPT by Simanchala Sarab(BABed,sem-4...larencebapu132 This is short and accurate description of World war-1 (1914-18)
It can give you the perfect factual conceptual clarity on the great war
Regards Simanchala Sarab
Student of BABed(ITEP, Secondary stage)in History at Guru Nanak Dev University Amritsar Punjab 🙏🙏
YSPH VMOC Special Report - Measles Outbreak Southwest US 4-26-2025.pptx
YSPH VMOC Special Report - Measles Outbreak Southwest US 4-26-2025.pptxYale School of Public Health - The Virtual Medical Operations Center (VMOC) A measles outbreak originating in West Texas has been linked to confirmed cases in New Mexico, with additional cases reported in Oklahoma and Kansas. The current case count is 771 from Texas, New Mexico, Oklahoma, and Kansas. 72 individuals have required hospitalization, and 3 deaths, 2 children in Texas and one adult in New Mexico. These fatalities mark the first measles-related deaths in the United States since 2015 and the first pediatric measles death since 2003.
The YSPH Virtual Medical Operations Center Briefs (VMOC) were created as a service-learning project by faculty and graduate students at the Yale School of Public Health in response to the 2010 Haiti Earthquake. Each year, the VMOC Briefs are produced by students enrolled in Environmental Health Science Course 581 - Public Health Emergencies: Disaster Planning and Response. These briefs compile diverse information sources – including status reports, maps, news articles, and web content– into a single, easily digestible document that can be widely shared and used interactively. Key features of this report include:
- Comprehensive Overview: Provides situation updates, maps, relevant news, and web resources.
- Accessibility: Designed for easy reading, wide distribution, and interactive use.
- Collaboration: The “unlocked" format enables other responders to share, copy, and adapt seamlessly.
The students learn by doing, quickly discovering how and where to find critical information and presenting it in an easily understood manner.
YSPH VMOC Special Report - Measles Outbreak Southwest US 4-30-2025.pptx
YSPH VMOC Special Report - Measles Outbreak Southwest US 4-30-2025.pptxYale School of Public Health - The Virtual Medical Operations Center (VMOC) A measles outbreak originating in West Texas has been linked to confirmed cases in New Mexico, with additional cases reported in Oklahoma and Kansas. The current case count is 795 from Texas, New Mexico, Oklahoma, and Kansas. 95 individuals have required hospitalization, and 3 deaths, 2 children in Texas and one adult in New Mexico. These fatalities mark the first measles-related deaths in the United States since 2015 and the first pediatric measles death since 2003.
The YSPH Virtual Medical Operations Center Briefs (VMOC) were created as a service-learning project by faculty and graduate students at the Yale School of Public Health in response to the 2010 Haiti Earthquake. Each year, the VMOC Briefs are produced by students enrolled in Environmental Health Science Course 581 - Public Health Emergencies: Disaster Planning and Response. These briefs compile diverse information sources – including status reports, maps, news articles, and web content– into a single, easily digestible document that can be widely shared and used interactively. Key features of this report include:
- Comprehensive Overview: Provides situation updates, maps, relevant news, and web resources.
- Accessibility: Designed for easy reading, wide distribution, and interactive use.
- Collaboration: The “unlocked" format enables other responders to share, copy, and adapt seamlessly. The students learn by doing, quickly discovering how and where to find critical information and presenting it in an easily understood manner.
Michelle Rumley & Mairéad Mooney, Boole Library, University College Cork. Tra...
Michelle Rumley & Mairéad Mooney, Boole Library, University College Cork. Tra...Library Association of Ireland
Presentation of the MIPLM subject matter expert Erdem Kaya
Presentation of the MIPLM subject matter expert Erdem KayaMIPLM Presentation of the MIPLM subject matter expert Erdem Kaya
The ever evoilving world of science /7th class science curiosity /samyans aca...
The ever evoilving world of science /7th class science curiosity /samyans aca...Sandeep Swamy The Ever-Evolving World of
Science
Welcome to Grade 7 Science4not just a textbook with facts, but an invitation to
question, experiment, and explore the beautiful world we live in. From tiny cells
inside a leaf to the movement of celestial bodies, from household materials to
underground water flows, this journey will challenge your thinking and expand
your knowledge.
Notice something special about this book? The page numbers follow the playful
flight of a butterfly and a soaring paper plane! Just as these objects take flight,
learning soars when curiosity leads the way. Simple observations, like paper
planes, have inspired scientific explorations throughout history.
Envenomation---Clinical Toxicology. pptx
Envenomation---Clinical Toxicology. pptxrekhapositivity Envenomation is the process by which venom is injected by the bite or sting of a venomous animal such as a snake, scorpion, spider, or insect. Arthropod bite is nothing but a sharp bite or sting by ants, fruit flies, bees, beetles, moths, or hornets. Though not a serious condition, arthropod bite can be extremely painful, with redness and mild to severe swelling around the site of the bite
To study the nervous system of insect.pptx
To study the nervous system of insect.pptxArshad Shaikh The *nervous system of insects* is a complex network of nerve cells (neurons) and supporting cells that process and transmit information. Here's an overview:
Structure
1. *Brain*: The insect brain is a complex structure that processes sensory information, controls behavior, and integrates information.
2. *Ventral nerve cord*: A chain of ganglia (nerve clusters) that runs along the insect's body, controlling movement and sensory processing.
3. *Peripheral nervous system*: Nerves that connect the central nervous system to sensory organs and muscles.
Functions
1. *Sensory processing*: Insects can detect and respond to various stimuli, such as light, sound, touch, taste, and smell.
2. *Motor control*: The nervous system controls movement, including walking, flying, and feeding.
3. *Behavioral responThe *nervous system of insects* is a complex network of nerve cells (neurons) and supporting cells that process and transmit information. Here's an overview:
Structure
1. *Brain*: The insect brain is a complex structure that processes sensory information, controls behavior, and integrates information.
2. *Ventral nerve cord*: A chain of ganglia (nerve clusters) that runs along the insect's body, controlling movement and sensory processing.
3. *Peripheral nervous system*: Nerves that connect the central nervous system to sensory organs and muscles.
Functions
1. *Sensory processing*: Insects can detect and respond to various stimuli, such as light, sound, touch, taste, and smell.
2. *Motor control*: The nervous system controls movement, including walking, flying, and feeding.
3. *Behavioral responses*: Insects can exhibit complex behaviors, such as mating, foraging, and social interactions.
Characteristics
1. *Decentralized*: Insect nervous systems have some autonomy in different body parts.
2. *Specialized*: Different parts of the nervous system are specialized for specific functions.
3. *Efficient*: Insect nervous systems are highly efficient, allowing for rapid processing and response to stimuli.
The insect nervous system is a remarkable example of evolutionary adaptation, enabling insects to thrive in diverse environments.
The insect nervous system is a remarkable example of evolutionary adaptation, enabling insects to thrive
One Hot encoding a revolution in Machine learning
One Hot encoding a revolution in Machine learningmomer9505 A brief introduction to ONE HOT encoding a way to communicate with machines
To study Digestive system of insect.pptx
To study Digestive system of insect.pptxArshad Shaikh Education is one thing no one can take away from you.”
Operations Management (Dr. Abdulfatah Salem).pdf
Operations Management (Dr. Abdulfatah Salem).pdfArab Academy for Science, Technology and Maritime Transport This version of the lectures is provided free of charge to graduate students studying the Operations Management course at the MBA level.
CBSE - Grade 8 - Science - Chemistry - Metals and Non Metals - Worksheet
CBSE - Grade 8 - Science - Chemistry - Metals and Non Metals - WorksheetSritoma Majumder Introduction
All the materials around us are made up of elements. These elements can be broadly divided into two major groups:
Metals
Non-Metals
Each group has its own unique physical and chemical properties. Let's understand them one by one.
Physical Properties
1. Appearance
Metals: Shiny (lustrous). Example: gold, silver, copper.
Non-metals: Dull appearance (except iodine, which is shiny).
2. Hardness
Metals: Generally hard. Example: iron.
Non-metals: Usually soft (except diamond, a form of carbon, which is very hard).
3. State
Metals: Mostly solids at room temperature (except mercury, which is a liquid).
Non-metals: Can be solids, liquids, or gases. Example: oxygen (gas), bromine (liquid), sulphur (solid).
4. Malleability
Metals: Can be hammered into thin sheets (malleable).
Non-metals: Not malleable. They break when hammered (brittle).
5. Ductility
Metals: Can be drawn into wires (ductile).
Non-metals: Not ductile.
6. Conductivity
Metals: Good conductors of heat and electricity.
Non-metals: Poor conductors (except graphite, which is a good conductor).
7. Sonorous Nature
Metals: Produce a ringing sound when struck.
Non-metals: Do not produce sound.
Chemical Properties
1. Reaction with Oxygen
Metals react with oxygen to form metal oxides.
These metal oxides are usually basic.
Non-metals react with oxygen to form non-metallic oxides.
These oxides are usually acidic.
2. Reaction with Water
Metals:
Some react vigorously (e.g., sodium).
Some react slowly (e.g., iron).
Some do not react at all (e.g., gold, silver).
Non-metals: Generally do not react with water.
3. Reaction with Acids
Metals react with acids to produce salt and hydrogen gas.
Non-metals: Do not react with acids.
4. Reaction with Bases
Some non-metals react with bases to form salts, but this is rare.
Metals generally do not react with bases directly (except amphoteric metals like aluminum and zinc).
Displacement Reaction
More reactive metals can displace less reactive metals from their salt solutions.
Uses of Metals
Iron: Making machines, tools, and buildings.
Aluminum: Used in aircraft, utensils.
Copper: Electrical wires.
Gold and Silver: Jewelry.
Zinc: Coating iron to prevent rusting (galvanization).
Uses of Non-Metals
Oxygen: Breathing.
Nitrogen: Fertilizers.
Chlorine: Water purification.
Carbon: Fuel (coal), steel-making (coke).
Iodine: Medicines.
Alloys
An alloy is a mixture of metals or a metal with a non-metal.
Alloys have improved properties like strength, resistance to rusting.
Niamh Lucey, Mary Dunne. Health Sciences Libraries Group (LAI). Lighting the ...
Niamh Lucey, Mary Dunne. Health Sciences Libraries Group (LAI). Lighting the ...Library Association of Ireland
Unit 4: Long term- Capital budgeting and its types
Unit 4: Long term- Capital budgeting and its typesbharath321164 budgeting: meaning, significance, process, project classification, and investment criteria: payback method, ARR method, net present value, IRR method, profitability
Metamorphosis: Life's Transformative Journey
Metamorphosis: Life's Transformative JourneyArshad Shaikh *Metamorphosis* is a biological process where an animal undergoes a dramatic transformation from a juvenile or larval stage to a adult stage, often involving significant changes in form and structure. This process is commonly seen in insects, amphibians, and some other animals.
YSPH VMOC Special Report - Measles Outbreak Southwest US 4-26-2025.pptx
YSPH VMOC Special Report - Measles Outbreak Southwest US 4-26-2025.pptxYale School of Public Health - The Virtual Medical Operations Center (VMOC)
YSPH VMOC Special Report - Measles Outbreak Southwest US 4-30-2025.pptx
YSPH VMOC Special Report - Measles Outbreak Southwest US 4-30-2025.pptxYale School of Public Health - The Virtual Medical Operations Center (VMOC)
Michelle Rumley & Mairéad Mooney, Boole Library, University College Cork. Tra...
Michelle Rumley & Mairéad Mooney, Boole Library, University College Cork. Tra...Library Association of Ireland
Operations Management (Dr. Abdulfatah Salem).pdf
Operations Management (Dr. Abdulfatah Salem).pdfArab Academy for Science, Technology and Maritime Transport
Niamh Lucey, Mary Dunne. Health Sciences Libraries Group (LAI). Lighting the ...
Niamh Lucey, Mary Dunne. Health Sciences Libraries Group (LAI). Lighting the ...Library Association of Ireland
Dynamic Programming
- 1. Presentation on By BHARAT BHUSHAN Asst. Professor, Department of Computer Sc. RLSY College, Ranchi Buddha Science and Technical Institute, Ranchi www.bharatsir.com Dynamic Programming in Algorithm Design Technique
- 2. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Algorithm Design Dynamic Programming Dynamic Programming (DP) is an algorithmic technique for solving an optimization problem by breaking it down into simpler sub problems and utilizing the fact that the optimal solution to the overall problem depends upon the optimal solution to its sub problems. Dynamic programming approach is similar to divide and conquer in breaking down the problem into smaller and yet smaller possible sub-problems. But unlike, divide and conquer, these sub-problems are not solved independently.
- 3. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Algorithm Design Dynamic Programming Approach Work The following are the steps that the dynamic programming follows: It breaks down the complex problem into simpler sub problems. It finds the optimal solution to these sub-problems. It stores the results of sub problems. The process of storing the results of sub problems is known as memorization. It reuses them so that same sub-problem is calculated more than once. Finally, calculate the result of the complex problem.
- 4. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Algorithm Design Approaches of Dynamic Programming There are two approaches to dynamic programming: Top-down approach Bottom-up approach Top-down approach : The top-down method solves the overall problem before you break it down into sub problems. This process works to solve larger problems by finding the solution to sub problems recursively, caching each result.
- 5. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Algorithm Design Approaches of Dynamic Programming Bottom-up approach : The bottom-up approach is also one of the techniques which can be used to implement the dynamic programming. It uses the tabulation technique to implement the dynamic programming approach. It solves the same kind of problems but it removes the recursion. If we remove the recursion, there is no stack overflow issue and no overhead of the recursive functions. In this tabulation technique, we solve the problems and store the results in a matrix.
- 6. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Algorithm Design Examples The following computer problems can be solved using dynamic programming approach − Fibonacci number series Knapsack problem Tower of Hanoi All pair shortest path by Floyd-Warshall Shortest path by Dijkstra Project scheduling
- 7. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Algorithm Design Advantages of Dynamic Programming 1. Dynamic programming can be used to obtain local as well as the total optimal solution. 2. Dynamic programming algorithms are generally compact code pieces as they are based on recursive functions. 3. The linear and non-linear problem, both kind of problems can be solved using dynamic programming. 4. Dynamic programming algorithms are easy to debug.
- 8. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Algorithm Design Disadvantages of Dynamic Programming 1. Dynamic programming uses recursion, which requires more memory in the call stack, and leads to a stack overflow condition in the runtime. 2. It takes memory to store the solutions of each sub-problem. There is no guarantee that the stored value will be used later in execution.
- 9. Bharat Bhushan, Assistant Professor, RLSY College, Ranchi Algorithm Design