35 algorithm-types

Algorithm classification Algorithms that use a similar problem-solving approach can be grouped together This classification scheme is neither exhaustive nor disjoint The purpose is not to be able to classify an algorithm as one type or another, but to highlight the various ways in which a problem can be attacked

A short list of categories Algorithm types we will consider include: Simple recursive algorithms Backtracking algorithms Divide and conquer algorithms Dynamic programming algorithms Greedy algorithms Branch and bound algorithms Brute force algorithms Randomized algorithms

Simple recursive algorithms I A simple recursive algorithm : Solves the base cases directly Recurs with a simpler subproblem Does some extra work to convert the solution to the simpler subproblem into a solution to the given problem I call these “simple” because several of the other algorithm types are inherently recursive

Example recursive algorithms To count the number of elements in a list: If the list is empty, return zero; otherwise, Step past the first element, and count the remaining elements in the list Add one to the result To test if a value occurs in a list: If the list is empty, return false; otherwise, If the first thing in the list is the given value, return true; otherwise Step past the first element, and test whether the value occurs in the remainder of the list

Backtracking algorithms Backtracking algorithms are based on a depth-first recursive search A backtracking algorithm: Tests to see if a solution has been found, and if so, returns it; otherwise For each choice that can be made at this point, Make that choice Recur If the recursion returns a solution, return it If no choices remain, return failure

Example backtracking algorithm To color a map with no more than four colors: color(Country n) If all countries have been colored (n > number of countries) return success; otherwise, For each color c of four colors, If country n is not adjacent to a country that has been colored c Color country n with color c recursivly color country n+1 If successful, return success Return failure (if loop exits)

Divide and Conquer A divide and conquer algorithm consists of two parts: Divide the problem into smaller subproblems of the same type, and solve these subproblems recursively Combine the solutions to the subproblems into a solution to the original problem Traditionally, an algorithm is only called divide and conquer if it contains two or more recursive calls

Examples Quicksort: Partition the array into two parts, and quicksort each of the parts No additional work is required to combine the two sorted parts Mergesort: Cut the array in half, and mergesort each half Combine the two sorted arrays into a single sorted array by merging them

Binary tree lookup Here’s how to look up something in a sorted binary tree: Compare the key to the value in the root If the two values are equal, report success If the key is less, search the left subtree If the key is greater, search the right subtree This is not a divide and conquer algorithm because, although there are two recursive calls, only one is used at each level of the recursion

Fibonacci numbers To find the n th Fibonacci number: If n is zero or one, return one; otherwise, Compute fibonacci(n-1) and fibonacci(n-2) Return the sum of these two numbers This is an expensive algorithm It requires O(fibonacci(n)) time This is equivalent to exponential time, that is, O(2 n )

Dynamic programming algorithms A dynamic programming algorithm remembers past results and uses them to find new results Dynamic programming is generally used for optimization problems Multiple solutions exist, need to find the “best” one Requires “optimal substructure” and “overlapping subproblems” Optimal substructure : Optimal solution contains optimal solutions to subproblems Overlapping subproblems : Solutions to subproblems can be stored and reused in a bottom-up fashion This differs from Divide and Conquer, where subproblems generally need not overlap

Fibonacci numbers again To find the n th Fibonacci number: If n is zero or one, return one; otherwise, Compute, or look up in a table, fibonacci(n-1) and fibonacci(n-2) Find the sum of these two numbers Store the result in a table and return it Since finding the n th Fibonacci number involves finding all smaller Fibonacci numbers, the second recursive call has little work to do The table may be preserved and used again later

Greedy algorithms An optimization problem is one in which you want to find, not just a solution, but the best solution A “greedy algorithm” sometimes works well for optimization problems A greedy algorithm works in phases: At each phase: You take the best you can get right now, without regard for future consequences You hope that by choosing a local optimum at each step, you will end up at a global optimum

Example: Counting money Suppose you want to count out a certain amount of money, using the fewest possible bills and coins A greedy algorithm would do this would be: At each step, take the largest possible bill or coin that does not overshoot Example: To make $6.39, you can choose: a $5 bill a $1 bill, to make $6 a 25¢ coin, to make $6.25 A 10¢ coin, to make $6.35 four 1¢ coins, to make $6.39 For US money, the greedy algorithm always gives the optimum solution

A failure of the greedy algorithm In some (fictional) monetary system, “krons” come in 1 kron, 7 kron, and 10 kron coins Using a greedy algorithm to count out 15 krons, you would get A 10 kron piece Five 1 kron pieces, for a total of 15 krons This requires six coins A better solution would be to use two 7 kron pieces and one 1 kron piece This only requires three coins The greedy algorithm results in a solution, but not in an optimal solution

Branch and bound algorithms Branch and bound algorithms are generally used for optimization problems As the algorithm progresses, a tree of subproblems is formed The original problem is considered the “root problem” A method is used to construct an upper and lower bound for a given problem At each node, apply the bounding methods If the bounds match, it is deemed a feasible solution to that particular subproblem If bounds do not match, partition the problem represented by that node, and make the two subproblems into children nodes Continue, using the best known feasible solution to trim sections of the tree, until all nodes have been solved or trimmed

Example branch and bound algorithm Travelling salesman problem: A salesman has to visit each of n cities (at least) once each, and wants to minimize total distance travelled Consider the root problem to be the problem of finding the shortest route through a set of cities visiting each city once Split the node into two child problems: Shortest route visiting city A first Shortest route not visiting city A first Continue subdividing similarly as the tree grows

Brute force algorithm A brute force algorithm simply tries all possibilities until a satisfactory solution is found Such an algorithm can be: Optimizing : Find the best solution. This may require finding all solutions, or if a value for the best solution is known, it may stop when any best solution is found Example: Finding the best path for a travelling salesman Satisficing : Stop as soon as a solution is found that is good enough Example: Finding a travelling salesman path that is within 10% of optimal

Improving brute force algorithms Often, brute force algorithms require exponential time Various heuristics and optimizations can be used Heuristic : A “rule of thumb” that helps you decide which possibilities to look at first Optimization : In this case, a way to eliminate certain possibilites without fully exploring them

Randomized algorithms A randomized algorithm uses a random number at least once during the computation to make a decision Example: In Quicksort, using a random number to choose a pivot Example: Trying to factor a large prime by choosing random numbers as possible divisors

35 algorithm-types

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35 algorithm-types

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