The document discusses regular languages and regular expressions. It begins by defining regular expressions over an alphabet and the basic operations of concatenation, union, and Kleene star. It then defines regular languages as the languages corresponding to regular expressions. Some examples of regular languages and expressions over the binary alphabet are given. It proves that the class of languages accepted by finite automata is equivalent to the class of regular languages. It also discusses properties like closure under operations and the pumping lemma for regular languages.
Longest common subsequence(dynamic programming).munawerzareef
The document discusses the longest common subsequence (LCS) problem. It defines key terms like subsequence and common subsequence. It presents the dynamic programming solution to find the longest common subsequence of two input sequences in O(nm) time using two nested for loops over the sequences' lengths, storing results in a 2D table and using pointers to recover the subsequence. It also provides pseudocode for the algorithm and discusses applications in biology.
Regular expressions describe regular languages using three operations: union, concatenation, and Kleene star. They can be defined recursively, where primitive expressions define single symbols and the empty string, and operations combine expressions. Regular expressions generate regular languages, and any regular language can be defined by a regular expression. Converting between regular expressions and finite automata allows regular languages to be represented in different standard forms.
The document describes pushdown automata (PDA) which are analogous to context-free languages in the same way that finite automata are analogous to regular languages. A PDA has states, input symbols, stack symbols, transition functions, an initial state, initial stack symbol, and accepting states. The transition function specifies state transitions based on the current state, input symbol, and top of stack symbol and can modify the stack. The document provides examples of PDAs for languages of the form wwr and balanced parentheses and discusses how PDAs work by changing their instantaneous descriptions as the input is processed and stack is modified.
- A minimum spanning tree (MST) connects all nodes in a graph with the minimum total edge weight while avoiding cycles.
- There are different algorithms that can find an MST, such as Kruskal's algorithm and Prim's algorithm, which were introduced in the document.
- Kruskal's algorithm works by sorting the edges by weight and building the MST by adding the shortest edges that do not create cycles. Prim's algorithm grows the MST from an initial node by always adding the shortest edge connected to the current MST.
The document discusses various greedy algorithms including knapsack problems, minimum spanning trees, shortest path algorithms, and job sequencing. It provides descriptions of greedy algorithms, examples to illustrate how they work, and pseudocode for algorithms like fractional knapsack, Prim's, Kruskal's, Dijkstra's, and job sequencing. Key aspects covered include choosing the best option at each step and building up an optimal solution incrementally using greedy choices.
The document discusses two types of knapsack problems - the 0-1 knapsack problem and the fractional knapsack problem. The 0-1 knapsack problem uses dynamic programming to determine how to fill a knapsack to maximize the total value of items without exceeding the knapsack's weight limit, where each item is either fully included or not included. The fractional knapsack problem allows partial inclusion of items and can be solved greedily by always including a fraction of the highest value per unit weight item until the knapsack is full.
This document provides an overview of deterministic finite automata (DFA) through examples and practice problems. It begins with defining the components of a DFA, including states, alphabet, transition function, start state, and accepting states. An example DFA is given to recognize strings ending in "00". Additional practice problems involve drawing minimal DFAs, determining the minimum number of states for a language, and completing partially drawn DFAs. The document aims to help students learn and practice working with DFA models.
This document discusses minimum spanning trees. It defines a minimum spanning tree as a spanning tree of a connected, undirected graph that has a minimum total cost among all spanning trees of that graph. The document provides properties of minimum spanning trees, including that they are acyclic, connect all vertices, and have n-1 edges for a graph with n vertices. Applications of minimum spanning trees mentioned include communication networks, power grids, and laying telephone wires to minimize total length.
Prim's algorithm is a greedy algorithm that finds a minimum spanning tree for a weighted undirected graph. It was developed in 1930 by Czech mathematician Vojtěch Jarník but later rediscovered and published by computer scientists Robert C. Prim in 1957 and Edsger W. Dijkstra in 1959. The algorithm finds the minimum spanning tree by building it one edge at a time, each time choosing the next edge that connects an isolated vertex to a partially constructed minimum spanning tree.
The document discusses recursive definitions of formal languages using regular expressions. It provides examples of recursively defining languages like INTEGER, EVEN, and factorial. Regular expressions can be used to concisely represent languages. The recursive definition of a regular expression is given. Examples are provided of regular expressions for various languages over an alphabet. Regular languages are those generated by regular expressions, and operations on regular expressions correspond to operations on the languages they represent.
The document describes pushdown automata (PDA). A PDA has a tape, stack, finite control, and transition function. It accepts or rejects strings by reading symbols on the tape, pushing/popping symbols on the stack, and changing state according to the transition function. The transition function defines the possible moves of the PDA based on the current state, tape symbol, and stack symbol. If the PDA halts in a final state with an empty stack, the string is accepted. PDAs can recognize any context-free language. Examples are given of PDAs for specific languages.
The 0-1 knapsack problem involves selecting items with given values and weights to maximize the total value without exceeding a weight capacity. It can be solved using a brute force approach in O(2^n) time or dynamic programming in O(n*c) time. Dynamic programming constructs a value matrix where each cell represents the maximum value for a given item and weight. The cell value is either from the item above or adding the item's value if the weight is less than remaining capacity. The last cell provides the maximum value solution.
This Lecture Note discusses the followings:
- What is NFA
- DFA vs NFA
- How does NFA compute
- Designing different NFA machines
- Regular operations on NFAs
- Conversion from NFA to DFA
The document discusses regular expressions and how they can be used to represent languages accepted by finite automata. It provides examples of how to:
1. Construct regular expressions from languages and finite state automata. Regular expressions can be built by defining expressions for subparts of a language and combining them.
2. Convert finite state automata to equivalent regular expressions using state elimination techniques. Intermediate states are replaced with regular expressions on transitions until a single state automaton remains.
3. Convert regular expressions to equivalent finite state automata by building epsilon-nondeterministic finite automata (ε-NFAs) based on the structure of the regular expression.
The document discusses minimum spanning trees and two algorithms for finding them: Prim's algorithm and Kruskal's algorithm. Prim's algorithm works by growing a spanning tree from an initial node, always adding the edge with the lowest weight that connects to a node not yet in the tree. Kruskal's algorithm sorts the edges by weight and builds up a spanning tree by adding edges in order as long as they do not form cycles. Both algorithms run on undirected, weighted graphs and produce optimal minimum spanning trees.
Greibach Normal Form (GNF) is a type of context-free grammar where the right-hand side of each production rule consists of a single terminal symbol followed by zero or more non-terminal symbols. The document discusses GNF, provides an example grammar in GNF, describes two lemmas used to convert grammars to GNF, and shows the procedure and steps to convert an arbitrary context-free grammar into GNF. It also provides examples of converting several grammars to GNF and solving related problems.
Single source stortest path bellman ford and dijkstraRoshan Tailor
This document discusses algorithms for finding shortest paths in weighted graphs:
- Dijkstra's algorithm finds single-source shortest paths in graphs with non-negative edge weights using a greedy approach and priority queue. It runs in O(ElogV) time with a Fibonacci heap.
- Bellman-Ford algorithm can handle graphs with negative edge weights by relaxing all edges V-1 times to detect negative cycles. It runs in O(VE) time.
- Examples are provided to illustrate the relaxation process and execution of both algorithms on sample graphs. Key properties like handling of negative weights and cycles are also explained.
This lecture slide contains:
1. Regular Languages
2. Regular Operations
3. Closure of regular languages
4. Regular expression
5. Precedence of regular operations
6. RE for different languages
7. RE to NFA conversion
8. DFA to GNFA to RE conversion
The Rabin-Karp string matching algorithm calculates a hash value for the pattern and for each substring of the text to compare values efficiently. If hash values match, it performs a character-by-character comparison, otherwise it skips to the next substring. This reduces the number of costly comparisons from O(MN) in brute force to O(N) on average by filtering out non-matching substrings in one comparison each using hash values. Choosing a large prime number when calculating hash values further decreases collisions and false positives.
This document provides an introduction and explanation of pushdown automata (PDA). It discusses how PDA are rooted in context-free grammar and can accept context-free languages (CFL). It explains that PDA have two types - deterministic (DPDA) and non-deterministic (NDPDA) - and that the power of NDPDA is greater than DPDA. The document also outlines the 7-tuple formal definition of a PDA and describes the basic functions of push, pop, and skip used to read a PDA. Examples of PDA are provided.
This document provides an overview of lecture topics on automata theory and formal languages. It discusses teaching procedures including lectures, assignments, quizzes, and exams. Key concepts are introduced such as formal vs informal languages, alphabets, strings, finite vs infinite languages, and operations like Kleene star and plus. Examples are given to illustrate language definitions using descriptive definitions and operations on alphabets. The document serves as an introduction to fundamental concepts in automata theory and formal languages that will be covered in more depth throughout the course lectures.
The document summarizes Kruskal's algorithm for finding a minimum cost spanning tree in a graph. Kruskal's algorithm uses a heap data structure to extract the lowest cost edge from the graph. It adds edges to the spanning tree only if they do not create cycles. This results in a minimum cost spanning tree being built incrementally. The document provides an example graph and shows the minimum cost spanning tree obtained using Kruskal's algorithm on that graph.
The document discusses loop invariants and uses insertion sort as an example. The invariant for insertion sort is that at the start of each iteration of the outer for loop, the elements in A[1...j-1] are sorted. It shows that this invariant is true before the first iteration, remains true after each iteration by how insertion sort works, and when the loops terminate the entire array A[1...n] will be sorted, proving correctness.
This is about a topic of compiler design, LR and SLR parsing algorithm and LR grammar, Canonical collection and Item, Conflict in LR parsing shift reduce. Classification of Bottom up parsing.
Automata theory is the study of abstract computing devices or "machines". The course will cover regular languages and their descriptors like finite state automata. It will also cover context free languages and their descriptors including context free grammars and pushdown automata. The course outline includes an introduction to models of computation, languages, and Turing machines and their variants. Students are encouraged to be active, ask questions, and think about what can and cannot be computed on different computational models.
Regular expressions are used to define the structure of tokens in a language. They are made up of symbols from a finite alphabet. A regular expression can be a single symbol, the empty string, alternation of two expressions, concatenation of two expressions, or Kleene closure of an expression. Deterministic finite automata (DFAs) are used to recognize languages defined by regular expressions. A DFA is defined by its states, input alphabet, start state, accepting states, and transition function between states based on input symbols. Examples show how to build DFAs to recognize languages defined by regular expressions.
This document discusses regular expressions and their properties. It begins by defining regular expressions and describing how they can be used to represent languages. It then discusses the languages associated with regular expressions and how to determine equivalence between regular expressions. The document also covers properties of regular expressions like precedence, algebraic laws, and identities. It provides examples of constructing finite automata from regular expressions and constructing regular expressions from finite automata. Finally, it discusses the pumping lemma and closure properties of regular languages.
Formal Languages and Automata Theory Unit 1Srimatre K
The document describes the minimization of a deterministic finite automaton (DFA) using the equivalence theorem. It involves partitioning the states into sets where states in the same set are indistinguishable based on their transitions. The initial partition P0 separates final and non-final states. Subsequent partitions P1, P2, etc. further split sets if states within are distinguishable. The process stops when the partition no longer changes, resulting in the minimized DFA with states merged within each final set. An example application of the steps to a 6-state DFA is also provided.
This document provides an overview of deterministic finite automata (DFA) through examples and practice problems. It begins with defining the components of a DFA, including states, alphabet, transition function, start state, and accepting states. An example DFA is given to recognize strings ending in "00". Additional practice problems involve drawing minimal DFAs, determining the minimum number of states for a language, and completing partially drawn DFAs. The document aims to help students learn and practice working with DFA models.
This document discusses minimum spanning trees. It defines a minimum spanning tree as a spanning tree of a connected, undirected graph that has a minimum total cost among all spanning trees of that graph. The document provides properties of minimum spanning trees, including that they are acyclic, connect all vertices, and have n-1 edges for a graph with n vertices. Applications of minimum spanning trees mentioned include communication networks, power grids, and laying telephone wires to minimize total length.
Prim's algorithm is a greedy algorithm that finds a minimum spanning tree for a weighted undirected graph. It was developed in 1930 by Czech mathematician Vojtěch Jarník but later rediscovered and published by computer scientists Robert C. Prim in 1957 and Edsger W. Dijkstra in 1959. The algorithm finds the minimum spanning tree by building it one edge at a time, each time choosing the next edge that connects an isolated vertex to a partially constructed minimum spanning tree.
The document discusses recursive definitions of formal languages using regular expressions. It provides examples of recursively defining languages like INTEGER, EVEN, and factorial. Regular expressions can be used to concisely represent languages. The recursive definition of a regular expression is given. Examples are provided of regular expressions for various languages over an alphabet. Regular languages are those generated by regular expressions, and operations on regular expressions correspond to operations on the languages they represent.
The document describes pushdown automata (PDA). A PDA has a tape, stack, finite control, and transition function. It accepts or rejects strings by reading symbols on the tape, pushing/popping symbols on the stack, and changing state according to the transition function. The transition function defines the possible moves of the PDA based on the current state, tape symbol, and stack symbol. If the PDA halts in a final state with an empty stack, the string is accepted. PDAs can recognize any context-free language. Examples are given of PDAs for specific languages.
The 0-1 knapsack problem involves selecting items with given values and weights to maximize the total value without exceeding a weight capacity. It can be solved using a brute force approach in O(2^n) time or dynamic programming in O(n*c) time. Dynamic programming constructs a value matrix where each cell represents the maximum value for a given item and weight. The cell value is either from the item above or adding the item's value if the weight is less than remaining capacity. The last cell provides the maximum value solution.
This Lecture Note discusses the followings:
- What is NFA
- DFA vs NFA
- How does NFA compute
- Designing different NFA machines
- Regular operations on NFAs
- Conversion from NFA to DFA
The document discusses regular expressions and how they can be used to represent languages accepted by finite automata. It provides examples of how to:
1. Construct regular expressions from languages and finite state automata. Regular expressions can be built by defining expressions for subparts of a language and combining them.
2. Convert finite state automata to equivalent regular expressions using state elimination techniques. Intermediate states are replaced with regular expressions on transitions until a single state automaton remains.
3. Convert regular expressions to equivalent finite state automata by building epsilon-nondeterministic finite automata (ε-NFAs) based on the structure of the regular expression.
The document discusses minimum spanning trees and two algorithms for finding them: Prim's algorithm and Kruskal's algorithm. Prim's algorithm works by growing a spanning tree from an initial node, always adding the edge with the lowest weight that connects to a node not yet in the tree. Kruskal's algorithm sorts the edges by weight and builds up a spanning tree by adding edges in order as long as they do not form cycles. Both algorithms run on undirected, weighted graphs and produce optimal minimum spanning trees.
Greibach Normal Form (GNF) is a type of context-free grammar where the right-hand side of each production rule consists of a single terminal symbol followed by zero or more non-terminal symbols. The document discusses GNF, provides an example grammar in GNF, describes two lemmas used to convert grammars to GNF, and shows the procedure and steps to convert an arbitrary context-free grammar into GNF. It also provides examples of converting several grammars to GNF and solving related problems.
Single source stortest path bellman ford and dijkstraRoshan Tailor
This document discusses algorithms for finding shortest paths in weighted graphs:
- Dijkstra's algorithm finds single-source shortest paths in graphs with non-negative edge weights using a greedy approach and priority queue. It runs in O(ElogV) time with a Fibonacci heap.
- Bellman-Ford algorithm can handle graphs with negative edge weights by relaxing all edges V-1 times to detect negative cycles. It runs in O(VE) time.
- Examples are provided to illustrate the relaxation process and execution of both algorithms on sample graphs. Key properties like handling of negative weights and cycles are also explained.
This lecture slide contains:
1. Regular Languages
2. Regular Operations
3. Closure of regular languages
4. Regular expression
5. Precedence of regular operations
6. RE for different languages
7. RE to NFA conversion
8. DFA to GNFA to RE conversion
The Rabin-Karp string matching algorithm calculates a hash value for the pattern and for each substring of the text to compare values efficiently. If hash values match, it performs a character-by-character comparison, otherwise it skips to the next substring. This reduces the number of costly comparisons from O(MN) in brute force to O(N) on average by filtering out non-matching substrings in one comparison each using hash values. Choosing a large prime number when calculating hash values further decreases collisions and false positives.
This document provides an introduction and explanation of pushdown automata (PDA). It discusses how PDA are rooted in context-free grammar and can accept context-free languages (CFL). It explains that PDA have two types - deterministic (DPDA) and non-deterministic (NDPDA) - and that the power of NDPDA is greater than DPDA. The document also outlines the 7-tuple formal definition of a PDA and describes the basic functions of push, pop, and skip used to read a PDA. Examples of PDA are provided.
This document provides an overview of lecture topics on automata theory and formal languages. It discusses teaching procedures including lectures, assignments, quizzes, and exams. Key concepts are introduced such as formal vs informal languages, alphabets, strings, finite vs infinite languages, and operations like Kleene star and plus. Examples are given to illustrate language definitions using descriptive definitions and operations on alphabets. The document serves as an introduction to fundamental concepts in automata theory and formal languages that will be covered in more depth throughout the course lectures.
The document summarizes Kruskal's algorithm for finding a minimum cost spanning tree in a graph. Kruskal's algorithm uses a heap data structure to extract the lowest cost edge from the graph. It adds edges to the spanning tree only if they do not create cycles. This results in a minimum cost spanning tree being built incrementally. The document provides an example graph and shows the minimum cost spanning tree obtained using Kruskal's algorithm on that graph.
The document discusses loop invariants and uses insertion sort as an example. The invariant for insertion sort is that at the start of each iteration of the outer for loop, the elements in A[1...j-1] are sorted. It shows that this invariant is true before the first iteration, remains true after each iteration by how insertion sort works, and when the loops terminate the entire array A[1...n] will be sorted, proving correctness.
This is about a topic of compiler design, LR and SLR parsing algorithm and LR grammar, Canonical collection and Item, Conflict in LR parsing shift reduce. Classification of Bottom up parsing.
Automata theory is the study of abstract computing devices or "machines". The course will cover regular languages and their descriptors like finite state automata. It will also cover context free languages and their descriptors including context free grammars and pushdown automata. The course outline includes an introduction to models of computation, languages, and Turing machines and their variants. Students are encouraged to be active, ask questions, and think about what can and cannot be computed on different computational models.
Regular expressions are used to define the structure of tokens in a language. They are made up of symbols from a finite alphabet. A regular expression can be a single symbol, the empty string, alternation of two expressions, concatenation of two expressions, or Kleene closure of an expression. Deterministic finite automata (DFAs) are used to recognize languages defined by regular expressions. A DFA is defined by its states, input alphabet, start state, accepting states, and transition function between states based on input symbols. Examples show how to build DFAs to recognize languages defined by regular expressions.
This document discusses regular expressions and their properties. It begins by defining regular expressions and describing how they can be used to represent languages. It then discusses the languages associated with regular expressions and how to determine equivalence between regular expressions. The document also covers properties of regular expressions like precedence, algebraic laws, and identities. It provides examples of constructing finite automata from regular expressions and constructing regular expressions from finite automata. Finally, it discusses the pumping lemma and closure properties of regular languages.
Formal Languages and Automata Theory Unit 1Srimatre K
The document describes the minimization of a deterministic finite automaton (DFA) using the equivalence theorem. It involves partitioning the states into sets where states in the same set are indistinguishable based on their transitions. The initial partition P0 separates final and non-final states. Subsequent partitions P1, P2, etc. further split sets if states within are distinguishable. The process stops when the partition no longer changes, resulting in the minimized DFA with states merged within each final set. An example application of the steps to a 6-state DFA is also provided.
The document discusses regular expressions and regular languages. It provides examples of regular expressions over alphabets, describes how to build regular expressions using operators like concatenation and Kleene star, and how regular expressions correspond to finite automata. It also discusses properties of regular languages and how to use the pumping lemma to prove that a language is not regular.
The document discusses compilers and their components. It begins by defining a compiler as a program that translates programs written in one language into another language. It then describes the main components of a compiler: lexical analysis, syntax analysis, semantic analysis, code generation, and code optimization. The document provides details on each component and how they work together in the compilation process from a source language to a target language.
Formal Languages and Automata Theory unit 2Srimatre K
This document provides information about regular expressions and finite automata. It includes:
1. A syllabus covering regular expressions, applications of regular expressions, algebraic laws, conversion of automata to expressions, and the pumping lemma.
2. Details of regular expressions including operators, precedence, applications, and algebraic laws.
3. How to convert between finite automata and regular expressions using Arden's theorem and state elimination methods.
4. Properties of regular languages including closure properties and how regular languages satisfy the pumping lemma.
1. Regular expressions are used to compactly represent sets of strings and are the basis for specifying patterns in lexical analysis.
2. Finite state automata are constructed to recognize strings belonging to the language defined by a regular expression. For every regular expression there is a corresponding finite state automaton.
3. The input string is checked for membership in the regular language by tracing a path through the automaton corresponding to the regular expression. If a complete path is found that reads the input string, it is accepted.
The document discusses regular expressions and finite automata. It defines regular expressions recursively and describes how they are used to represent sets of strings called regular languages. Operations on regular expressions like union, concatenation, and Kleene closure are covered. It also discusses how to convert between finite automata and regular expressions in both directions using techniques like Arden's theorem. Properties of regular languages like closure and the pumping lemma are presented.
Deterministic Finite State Automata (DFAs) are machines that read input strings and determine whether to accept or reject them based on their state transitions. A DFA is defined as a 5-tuple (Q, Σ, δ, q0, F) where Q is a finite set of states, Σ is a finite input alphabet, q0 is the starting state, F is the set of accepting states, and δ is the transition function that maps a state and input symbol to the next state. The language accepted by a DFA is the set of strings that cause the DFA to enter an accepting state. Nondeterministic Finite State Automata (NFAs) are similar but δ maps to sets of states rather
Deterministic Finite State Automata (DFAs) are machines that read input strings and determine whether to accept or reject them based on their state transitions. A DFA is defined as a 5-tuple (Q, Σ, δ, q0, F) where Q is a finite set of states, Σ is a finite input alphabet, q0 is the starting state, F is the set of accepting states, and δ is the transition function that maps a state and input symbol to the next state. The language accepted by a DFA is the set of strings that cause the DFA to enter an accepting state. Nondeterministic Finite State Automata (NFAs) are similar but δ maps to sets of states rather
This document provides an introduction to automata theory and finite automata. It defines an automaton as an abstract computing device that follows a predetermined sequence of operations automatically. A finite automaton has a finite number of states and can be deterministic or non-deterministic. The document outlines the formal definitions and representations of finite automata. It also discusses related concepts like alphabets, strings, languages, and the conversions between non-deterministic and deterministic finite automata. Methods for minimizing deterministic finite automata using Myhill-Nerode theorem and equivalence theorem are also introduced.
This document discusses regular languages and finite automata. It begins by defining regular languages and expressions, and describing the equivalence between non-deterministic finite automata (NFAs) and deterministic finite automata (DFAs). It then discusses converting between regular expressions, NFAs with epsilon transitions, NFAs without epsilon transitions, and DFAs. The document provides examples of regular expressions and conversions between different representations. It concludes by describing the state elimination, formula, and Arden's methods for converting a DFA to a regular expression.
This document contains the solutions to homework 3 for the course CS 341: Foundations of Computer Science II. It includes solutions to problems involving constructing NFAs and DFAs for various languages, proving properties of NFAs and regular expressions, and constructing regular expressions and DFAs.
CS 162 Fall 2015 Homework 1 Problems September 29, 2015 Timothy Johnson 1. Ex...parmeet834
CS 162 Fall 2015
Homework 1 Problems
September 29, 2015 Timothy Johnson
1. Exercise 2.2.4 on page 53 of Hopcroft et al. Give DFA’s accepting the following languages over the alphabet {0,1}. (a) The set of all strings ending in 0
Regular expressions-Theory of computationBipul Roy Bpl
Regular expressions are a notation used to specify formal languages by defining patterns over strings. They are declarative and can describe the same languages as finite automata. Regular expressions are composed of operators for union, concatenation, and Kleene closure and can be converted to equivalent non-deterministic finite automata and vice versa. They also have an algebraic structure with laws governing how expressions combine and simplify.
1. Regular expressions provide a declarative way to express patterns in strings and are commonly used in Unix environments and programming languages like Perl.
2. Regular expressions represent regular languages that can also be represented by finite state machines. There is a correspondence between regular expressions and finite state machines such that any regular expression can be converted to a finite state machine and vice versa.
3. Regular expressions are recursively defined based on operators like union, concatenation, and Kleene closure. The precedence of operators is also specified with Kleene closure having the highest precedence followed by concatenation and union.
This document provides solutions to homework problems related to finite automata. It includes:
1) DFAs recognizing languages based on string length properties.
2) An NFA recognizing a union of languages over the alphabet {a,b}.
3) A proof that the reverse of a regular language is also regular by constructing an NFA.
4) DFAs recognizing languages based on the lengths of runs in strings.
5) A proof that the intersection of a regular language with a subset alphabet is regular.
6) Converting an NFA to an equivalent DFA and constructing an NFA with a single final state.
This document discusses regular expressions and finite automata. It begins by defining regular expressions and their applications. Next, it describes the recursive definition of regular expressions and their operators. It then discusses writing regular expressions using Kleene star and plus. The document also covers identities for regular expressions, Arden's theorem, and how to convert between finite automata and regular expressions. In particular, it provides methods for constructing finite automata that accept unions, concatenations, and closures of regular languages.
The Pala kings were people-protectors. In fact, Gopal was elected to the throne only to end Matsya Nyaya. Bhagalpur Abhiledh states that Dharmapala imposed only fair taxes on the people. Rampala abolished the unjust taxes imposed by Bhima. The Pala rulers were lovers of learning. Vikramshila University was established by Dharmapala. He opened 50 other learning centers. A famous Buddhist scholar named Haribhadra was to be present in his court. Devpala appointed another Buddhist scholar named Veerdeva as the vice president of Nalanda Vihar. Among other scholars of this period, Sandhyakar Nandi, Chakrapani Dutta and Vajradatta are especially famous. Sandhyakar Nandi wrote the famous poem of this period 'Ramcharit'.
How to manage Multiple Warehouses for multiple floors in odoo point of saleCeline George
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How to Subscribe Newsletter From Odoo 18 WebsiteCeline George
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K12 Tableau Tuesday - Algebra Equity and Access in Atlanta Public Schoolsdogden2
Algebra 1 is often described as a “gateway” class, a pivotal moment that can shape the rest of a student’s K–12 education. Early access is key: successfully completing Algebra 1 in middle school allows students to complete advanced math and science coursework in high school, which research shows lead to higher wages and lower rates of unemployment in adulthood.
Learn how The Atlanta Public Schools is using their data to create a more equitable enrollment in middle school Algebra classes.
Odoo Inventory Rules and Routes v17 - Odoo SlidesCeline George
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How to Manage Opening & Closing Controls in Odoo 17 POSCeline George
In Odoo 17 Point of Sale, the opening and closing controls are key for cash management. At the start of a shift, cashiers log in and enter the starting cash amount, marking the beginning of financial tracking. Throughout the shift, every transaction is recorded, creating an audit trail.
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
How to track Cost and Revenue using Analytic Accounts in odoo Accounting, App...Celine George
Analytic accounts are used to track and manage financial transactions related to specific projects, departments, or business units. They provide detailed insights into costs and revenues at a granular level, independent of the main accounting system. This helps to better understand profitability, performance, and resource allocation, making it easier to make informed financial decisions and strategic planning.
INTRO TO STATISTICS
INTRO TO SPSS INTERFACE
CLEANING MULTIPLE CHOICE RESPONSE DATA WITH EXCEL
ANALYZING MULTIPLE CHOICE RESPONSE DATA
INTERPRETATION
Q & A SESSION
PRACTICAL HANDS-ON ACTIVITY
Title: A Quick and Illustrated Guide to APA Style Referencing (7th Edition)
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Exploring Substances:
Acidic, Basic, and
Neutral
Welcome to the fascinating world of acids and bases! Join siblings Ashwin and
Keerthi as they explore the colorful world of substances at their school's
National Science Day fair. Their adventure begins with a mysterious white paper
that reveals hidden messages when sprayed with a special liquid.
In this presentation, we'll discover how different substances can be classified as
acidic, basic, or neutral. We'll explore natural indicators like litmus, red rose
extract, and turmeric that help us identify these substances through color
changes. We'll also learn about neutralization reactions and their applications in
our daily lives.
by sandeep swamy
2. Topics to be covered
Regular expressions & Regular
Languages relationship
Reduction of states
Pumping Lemma
Day 5
3. Regular languages
● Any language that can be depicted(expressed) using a Finite State
machine is called a Regular language.
● A FSM can’t store any input variable, nor can it count.
● Hence any language that requires memory is not a regular language
7. Regular languages and Regular Expressions
● A regular language can be described using regular expressions
consisting of the symbols such as alphabets in Σ.
● Additionally consisting of operators like ‘.’ | ‘+’ | ‘*’
● The symbols ‘(’ and ‘)’ can be used with regular expressions.
8. Regular languages and Regular Expressions
● + operator(union), has the least precedence.
● . operator(concatenation) has mid precedence.
● * operator(closure) has highest precedence.
However the expression enveloped by parentheses
obtains the highest precedence.
Let us try to understand the precedence of these operators:
9. Regular languages and Regular Expressions
A regular expression is recursively defined as follows:
1. Φ is a regular expression denoting an empty language.
2. ε-(epsilon) is a regular expression indicates the language containing an empty string.
3. a is a regular expression which indicates the language containing only {a}
4. If R is a regular expression denoting the language LR and S is a regular expression denoting
the language Ls, then
a. R+S is a regular expression corresponding to the language LR
U LS
.
b. R.S is a regular expression corresponding to the languageLR
. LS
.
c. R* is a regular expression corresponding to the language LR
.
5. The expressions obtained by applying any of the rules from 1 to 4 are regular expressions.
10. Regular languages and Regular Expressions
a* String consisting of any number of a’s(0 or more)
a
+ String consisting of at least of a’s
a+b String consisting of either one a or one b
(a+b)* Set of strings of a’s and b’s of any length(NULL included)
(a+b)*abb Set of strings of a’s and b’s, ending with abb
ab(a+b)* Set of strings of a’s and b’s, starting with ab
11. Regular languages and Regular Expressions
(a+b)*aa(a+b)* Set of strings of a’s and b’s, having substring aa
a*b*c* String consisting of any number of a’s(0 or more) followed by any
number of b’s(0 or more) followed by any number of c’s(0 or more)
a
+
b
+
c
+ String consisting of at least 1 a, followed by string having at least 1
b, followed by string having at least 1 c
aa*bb*cc* String consisting of at least 1 a, followed by string having at least 1
b, followed by string having at least 1 c
(a+b)*(a+bb) Set of strings of a’s and b’s ending with either a or bb
(aa)*(bb)*b Set of strings of even number of a’s followed by odd number of b’s
13. Regular Expression Problems
Q. Obtain a regular expression having a’s and b’s having length 2.
Strings of a’s and b’s having length 2: aa, bb, ab, ba
RE is: (aa+bb+ab+ba)
14. Regular Expression Problems
Q. Obtain a regular expression having a’s and b’s having length <=2.
Strings of a’s and b’s having length <=2:
ε + a + b + aa + bb + ab + ba
This can be written as: (ε + a + b)(ε + a + b)
RE is: (ε + a + b)2
15. Regular Expression Problems
Q. Obtain a regular expression having a’s and b’s having length <=10.
From the logic of the previous problem
RE is: (ε + a + b)10
16. Regular Expression Problems
Q. Obtain a regular expression having a’s and b’s having even length.
To obtain this we will use strings aa,bb,ab,ba zero
or more times.
RE is: (aa+bb+ab+ba)*
This can also be written as:
RE is: ((a+b)(a+b))*
17. Regular Expression Problems
Q. Obtain a regular expression having a’s and b’s having odd length.
From the previous example we know that the RE
for even length is ((a+b)(a+b))*
To this we just need to add 1 more symbol.
Hence,
RE is: (a+b)((a+b)(a+b))*
18. Regular Expression Problems
Q. Obtain a regular expression having alternate a’s and b’s.
To get alternate a’s and b’s we can use multiple
concatenations of ‘ab’ or ‘ba’.
Additionally to take care of the starting and ending
variable to be either a or b, we add (ε+a) and (ε+b)
In appropriate places.
RE is: (ε+b)(ab)*(ε+a)
19. To obtain a FA from a RE
We have seen examples of obtaining finite automata from a regular
expression. Now let us see this conversion from the other end.
Here we have the schematic
Representation of a Finite Automata(M)
accepting a regular Expression(R).
Where q is the initial and
f is the final state.
20. To obtain a FA from a RE
Let’s see the various cases for conversion.
21. To obtain a FA from a RE
Let’s see the various cases for conversion.
22. To obtain a FA from a RE
Let’s see the various cases for conversion.
Case 3: R=(R1
)* we can construct a NFA that accepts L((R1
)*) as shown in fig 2.6
23. To obtain a FA from a RE
Obtain an FSM for RE: a* + b* + c*
24. To obtain a FA from a RE
Adding the three graphs obtained for a*, b*, c*
25. To obtain a FA from a RE
Obtain a FSM for (a+b)*aa(a+b)*
26. To obtain a FA from a RE
We obtain FA for ‘aa’, and concatenate with (a+b)*
27. To obtain a FA from a RE
Finally obtained FSM is:
28. Pumping Lemma and Regular Languages
Pumping Lemma for Regular Languages
For any regular language L, there exists an integer n, such that for all
x ∈ L with |x| ≥ n, there exists u, v, w ∈ Σ*, such that x = uvw, and
(1) |uv| ≤ n
(2) |v| ≥ 1
(3) for all i ≥ 0: u vi
w ∈ L
29. Pumping Lemma and Regular Languages
Show that L = {w.wR
| w ∈(0,1)*} is not regular
30. Pumping Lemma and Regular Languages
Show that L = {ai
bj
| i>j } is not regular
31. Minimization of DFA
Suppose there is a DFA D = { Q, Σ, q0
, δ, F } which recognizes a language L.
Then the minimized DFA D’ = { Q’, Σ, q0
, δ’, F’ } can be constructed for language L as:
Step 1: We will divide Q (set of states) into two sets. One set will contain all final states and other set
will contain non-final states. This partition is called P0
.
Step 2: Initialize k = 1
Step 3: Find Pk
by partitioning the different sets of Pk-1
. In each set of Pk-1
, we will take all possible
pair of states. If two states of a set are distinguishable, we will split the sets into different sets in Pk
.
Step 4: Stop when Pk
= Pk-1
(No change in partition)
Step 5: All states of one set are merged into one. No. of states in minimized DFA will be equal to no.
of sets in Pk
.
32. Minimization of DFA
Step 1: P0
will have two sets of states.
One set will contain q1, q2, q4 which are
final states of DFA and another set will
contain remaining states.
So P0
= { { q1, q2, q4 }, { q0, q3, q5 } }.
Step 2. To calculate P1, we will check
whether sets of partition P0 can be partitioned or not:
i) For set { q1, q2, q4 } :
δ ( q1, 0 ) = δ ( q2, 0 ) = q2 and
δ ( q1, 1 ) = δ ( q2, 1 ) = q5.
So q1 and q2 are not distinguishable.
Similarly, δ ( q1, 0 ) = δ ( q4, 0 ) = q2 and
δ ( q1, 1 ) = δ ( q4, 1 ) = q5.
So q1 and q4 are not distinguishable.
33. Minimization of DFA
ii) For set { q0, q3, q5 } :
δ ( q0, 0 ) = q3 and δ ( q3, 0 ) = q0
δ ( q0, 1) = q1 and δ( q3, 1 ) = q4
So, q0 and q3 are not distinguishable.
δ ( q0, 0 ) = q3 and δ ( q5, 0 ) = q5 and
δ ( q0, 1 ) = q1 and δ ( q5, 1 ) = q5
Moves of q0 and q5 on input symbol 1 are q3 and q5
respectively which are in different set in partition P0. So, q0 and
q5 are distinguishable. So, set { q0, q3, q5 } will be partitioned
into { q0, q3 } and { q5 }. So,
P1 = { { q1, q2, q4 }, { q0, q3}, { q5 } }
34. Minimization of DFA
To calculate P2, we will check whether
sets of partition P1 can be partitioned or not:
iii)For set { q1, q2, q4 } :
δ ( q1, 0 ) = δ ( q2, 0 ) = q2 and
δ ( q1, 1 ) = δ ( q2, 1 ) = q5.
So q1 and q2 are not distinguishable.
Similarly, δ ( q1, 0 ) = δ ( q4, 0 ) = q2 and
δ ( q1, 1 ) = δ ( q4, 1 ) = q5.
So q1 and q4 are not distinguishable.
So, { q1, q2, q4 } set will not be partitioned in P2.
35. Minimization of DFA
iv)For set { q0, q3 } :
δ ( q0, 0 ) = q3 and δ ( q3, 0 ) = q0
δ ( q0, 1 ) = q1 and δ ( q3, 1 ) = q4
Moves of q0 and q3 on input symbol 0 are
q3 and q0 respectively which are in same set in
partition P1. Similarly, Moves of q0 and q3 on input
symbol 1 are q3 and q0 which are in same set in
partition P1. So, q0 and q3 are not distinguishable.
36. Minimization of DFA
v) For set { q5 }:
Since we have only one state in this set, it can’t be
further partitioned. So,
P2 = { { q1, q2, q4 }, { q0, q3 }, { q5 } }
Since, P1=P2. So, this is the final partition.
Partition P2 means that q1, q2 and q4 states are
merged into one. Similarly, q0 and q3 are merged into one.