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Chapter 5 Fundamental Algorithm Design Techniques

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Overview The Greedy Method (5.1) Divide and Conquer (5.2) Dynamic Programming (5.3)

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Greedy Outline The Greedy Method Technique (§5.1) Fractional Knapsack Problem (§5.1.1) Task Scheduling (§5.1.2) Minimum Spanning Trees (§7.3) [future lecture]

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A New Vending Machine? A new Coke machine in the lounge behind the ulab? Being efficient computer scientists, we want to return the smallest possible number of coins in change

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The Greedy Method

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The greedy method is a general algorithm design paradigm, built on the following elements: configurations: different choices, collections, or values to find objective function: a score assigned to configurations, which we want to either maximize or minimize It works best when applied to problems with the greedy-choice property: a globally-optimal solution can always be found by a series of local improvements from a starting configuration.

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Making Change Problem: A dollar amount to reach and a collection of coin amounts to use to get there. Configuration: A dollar amount yet to return to a customer plus the coins already returned Objective function: Minimize number of coins returned. Greedy solution: Always return the largest coin you can Example 1: Coins are valued $.32, $.08, $.01 Has the greedy-choice property, since no amount over $.32 can be made with a minimum number of coins by omitting a $.32 coin (similarly for amounts over $.08, but under $.32). Example 2: Coins are valued $.30, $.20, $.05, $.01 Does not have greedy-choice property, since $.40 is best made with two $.20’s, but the greedy solution will pick three coins (which ones?)

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How to rob a bank…

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The Fractional Knapsack Problem Given: A set S of n items, with each item i having b i - a positive benefit w i - a positive weight Goal: Choose items with maximum total benefit but with weight at most W. If we are allowed to take fractional amounts, then this is the fractional knapsack problem. In this case, we let x i denote the amount we take of item i Objective: maximize Constraint:

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Example Given: A set S of n items, with each item i having b i - a positive benefit w i - a positive weight Goal: Choose items with maximum total benefit but with weight at most W. Weight: Benefit: ml8 ml2 ml6 ml1 ml $12$32$40$30$50 Items: Value: 3 ($ per ml) ml Solution: 1 ml of 5 2 ml of 3 6 ml of 4 1 ml of 2 “knapsack”

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The Fractional Knapsack Algorithm Greedy choice: Keep taking item with highest value (benefit to weight ratio) Since Run time: O(n log n). Why? Correctness: Suppose there is a better solution there is an item i with higher value than a chosen item j, but x i 0 and v i

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Task Scheduling Given: a set T of n tasks, each having: A start time, s i A finish time, f i (where s i < f i ) Goal: Perform all the tasks using a minimum number of “machines.”

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Task Scheduling Algorithm Greedy choice: consider tasks by their start time and use as few machines as possible with this order. Run time: O(n log n). Why? Correctness: Suppose there is a better schedule. We can use k-1 machines The algorithm uses k Let i be first task scheduled on machine k Machine i must conflict with k-1 other tasks But that means there is no non- conflicting schedule using k-1 machines Algorithm taskSchedule(T) Input: set T of tasks w/ start time s i and finish time f i Output: non-conflicting schedule with minimum number of machines m 0 {no. of machines} while T is not empty remove task i w/ smallest s i if there’s a machine j for i then schedule i on machine j else m m + 1 schedule i on machine m

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Example Given: a set T of n tasks, each having: A start time, s i A finish time, f i (where s i < f i ) [1,4], [1,3], [2,5], [3,7], [4,7], [6,9], [7,8] (ordered by start) Goal: Perform all tasks on min. number of machines Machine 1 Machine 3 Machine 2

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Divide-and-Conquer 7 2 9 4 2 2 79 4 72 29 94 4

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Outline and Reading Divide-and-conquer paradigm (§5.2) Review Merge-sort (§4.1.1) Recurrence Equations (§5.2.1) Iterative substitution Recursion trees Guess-and-test The master method Integer Multiplication (§5.2.2)

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Divide-and-Conquer Divide-and conquer is a general algorithm design paradigm: Divide: divide the input data S in two or more disjoint subsets S 1, S 2, … Recur: solve the subproblems recursively Conquer: combine the solutions for S 1, S 2, …, into a solution for S The base case for the recursion are subproblems of constant size Analysis can be done using recurrence equations

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Binary Search I have a number X between c 1 and c 2 … Divide and conquer algorithm? Analysis: Called a Recurrence Equation How to solve?

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Solving the Binary Search Recurrence T(n) = T(n/2) + c T(n/2) = T(n/4) + c T(n/4) = T(n/8) + c … T(n) = T(n/2) + c = [T(n/4) + c] + c = T(n/2 2 ) + 2 c = T(n/2 3 ) + 3 c [telescope] = T(n/2 k ) + k c done when k=lg n; then T(n/2 k )=T(1) is base case = b + c lg n

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Merge Sort Recurrence Equation Analysis Recurrence Equation? We can analyze by finding a closed form solution. Method: expand and telescope

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Solving the Merge Sort recurrence T(n) = 2T(n/2) + b n T(n/2) = 2T(n/4) + b n/2 T(n/4) = 2T(n/8) + b n/4 … T(n) = 2T(n/2) + b n = 2[2T(n/4) + b n/2] + b n = 2 2 T(n/2 2 ) + 2 b n = 2 3 T(n/2 3 ) + 3 b n [telescope] = 2 k T(n/2 k ) + k b n done when k=lg n; then T(n/2 k )=T(1) is base case = b n + b n lg n

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Min & Max Given a set of n items, find the min and max. How many comparisons needed? Simple alg: n-1 comparisons to find min, n-1 for max Recursive algorithm: void minmax(int i, int j, int& min0, int& max0) { if (j<=i+1) [handle base case; return] m=(i+j+1)/2; minmax(i,m-1,min1,max1); minmax(m,j,min2,max2); min0 = min(min1,min2); max0 = max(max1,max2); } Recurrence:T(n)=2T(n/2) + 2; T(2)=1 Solution?1.5 n - 2

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Solving the Min&Max Recurrence T(n) = 2T(n/2) + 2; T(2)=1 T(n/2) = 2T(n/4) + 2 T(n/4) = 2T(n/8) + 2 … T(n) = 2T(n/2) + 2 = 2[2T(n/4) + 2] + 2 = 2 2 T(n/2 2 ) = 2 3 T(n/2 3 ) [telescope] = 2 k T(n/2 k ) + = 2 k T(n/2 k ) + 2 k done when k=(lg n) - 1; then T(n/2 k )=T(2) is base case = n/2 + n – 2 = 1.5n - 2

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Hanoi runtime? void hanoi(n, from, to, spare { if (n > 0) { hanoi(n-1,from,spare,to); cout << from << “ – “ << to << endl; hanoi(n-1,spare,to,from); } T(n) = 2T(n-1) + c T(0) = b Look it up: T(n) = O(2 n )

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Master Method Another way to solve recurrences: “Look it up” The Master Theorem solves many of them: If T(n)=c, n

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An Important use of Recurrence Relations by Donald KnuthDonald Knuth

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More Divide and Conquer Examples pow(x,n) log(n,base) Point-in-convex-polygon Fractals

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Creating Fractals Koch Snowflake Fractal Star Your own…

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Integer Multiplication Algorithm: Multiply two n-bit integers I and J. Divide step: Split I and J into high-order and low-order bits We can then define I*J by multiplying the parts and adding: So, T(n) = 4T(n/2) + n, which implies T(n) is O(n 2 ). But that is no better than the algorithm we learned in grade school.

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An Improved Integer Multiplication Algorithm Algorithm: Multiply two n-bit integers I and J. Divide step: Split I and J into high-order and low-order bits Observe that there is a different way to multiply parts: So, T(n) = 3T(n/2) + n, which implies T(n) is O(n log 2 3 ) [by master theorem]. Thus, T(n) is O(n ).

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Practice Analyzing Recursive Functions 1. int fun1(int n) { if (n==1) return 50; return fun1(n/2) * fun1(n/2) + 97; } 2. int fun2(int n) { if (n==1) return 1; int sum=0; for (int i=1; i<=n; i++) sum += fun(n-1); return sum; }

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Dynamic Programming

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Outline and Reading Fibonacci Making Change Matrix Chain-Product (§5.3.1) The General Technique (§5.3.2) 0-1 Knapsack Problem (§5.3.3)

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Opinions? public class Fib1 { public static BigInteger fibonacci(long n) { if (n <= 2) return new BigInteger("1"); return fibonacci(n-1).add(fibonacci(n-2)); } public static void main( String args[] ) { long n = Integer.parseInt(args[0]); System.out.println("Fib(" + n + ") = " + fibonacci(n).toString()); }

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Bottom up… public class Fib2 { static BigInteger[] fibs; public static BigInteger fibonacci(int n) { BigInteger result; fibs[1]=new BigInteger("1"); fibs[2]=new BigInteger("1"); for (int i=3; i<=n; i++) fibs[i] = fibs[i-1].add(fibs[i-2]); return fibs[n]; } public static void main( String args[] ) { int n = Integer.parseInt(args[0]); fibs = new BigInteger[n+1]; System.out.println("Fib(" + n + ") = " + fibonacci(n).toString()); }

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Memoize… public class Fib3 { static BigInteger[] fibs; public static BigInteger fibonacci(int n) { long result; if (fibs[n] != null) return fibs[n]; else { fibs[n] = fibonacci(n-1).add(fibonacci(n-2)); return fibs[n]; } public static void main( String args[] ) { int n = Integer.parseInt(args[0]); fibs = new BigInteger [n+1]; fibs[1] = new BigInteger("1"); fibs[2] = new BigInteger("1"); System.out.println("Fib("+n+") = " + fibonacci(n)); }

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Dynamic Programming Applies to problems that can be solved recursively, but the subproblems overlap (Typically optimization problems) Method: Write definition of value you want to compute Express solution in terms of sub-problem solutions Compute bottom-up or “memoize”

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Making Change Being good Computer Scientists, we want to have a General Solution for making change in our vending machine. Suppose that our coin values were 40, 30, 5, and 1 cent? How would me make change using the smallest number of coins?

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Making Change – DP solution Suppose we have coin values c 1, c 2, …, c k and we want to make change for value V using the smallest number of coins? What sub-problem solutions would give us solution to the whole problem? Define NC(V) = smallest number of coins that will add up to V. Base case? Run time? Some kind of exponential, if you’re not careful… How can we implement this efficiently?

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Dynamic Programming: Two methods for efficiency Build a table bottom-up Compute NC(1), NC(2), …, NC(V), storing sub- problem results in a table as you go up Memoize Compute NC(V) recursively, but store new sub- problem results when you first compute them

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Matrix Chain-Products Dynamic Programming is a general algorithm design paradigm. Rather than give the general structure, let us first give a motivating example: Matrix Chain-Products Review: Matrix Multiplication. C = A*B A is d × e and B is e × f O(def ) time AC B dd f e f e i j i,j

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Matrix Chain-Products Matrix Chain-Product: Compute A=A 0 *A 1 *…*A n-1 A i is d i × d i+1 Problem: How to parenthesize? Example B is 3 × 100 C is 100 × 5 D is 5 × 5 (B*C)*D takes = 1575 ops B*(C*D) takes = 4000 ops

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An Enumeration Approach Matrix Chain-Product Alg.: Try all possible ways to parenthesize A=A 0 *A 1 *…*A n-1 Calculate number of ops for each one Pick the one that is best Running time: The number of parethesizations is equal to the number of binary trees with n nodes This is exponential! It is called the Catalan number, and it is almost 4 n. This is a terrible algorithm!

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A Greedy Approach Idea #1: repeatedly select the product that uses (up) the most operations. Counter-example: A is 10 × 5 B is 5 × 10 C is 10 × 5 D is 5 × 10 Greedy idea #1 gives (A*B)*(C*D), which takes = 2000 ops A*((B*C)*D) takes = 1000 ops

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Another Greedy Approach Idea #2: repeatedly select the product that uses the fewest operations. Counter-example: A is 101 × 11 B is 11 × 9 C is 9 × 100 D is 100 × 99 Greedy idea #2 gives A*((B*C)*D)), which takes = ops (A*B)*(C*D) takes = ops The greedy approach is not giving us the optimal value.

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A “Recursive” Approach Define subproblems: Find the best parenthesization of A i *A i+1 *…*A j. Let N i,j denote the number of operations done by this subproblem. The optimal solution for the whole problem is N 0,n-1. Subproblem optimality: The optimal solution can be defined in terms of optimal subproblems There has to be a final multiplication (root of the expression tree) for the optimal solution. Say, the final multiply is at index i: (A 0 *…*A i )*(A i+1 *…*A n-1 ). Then the optimal solution N 0,n-1 is the sum of two optimal subproblems, N 0,i and N i+1,n-1 plus the time for the last multiply. If the global optimum did not have these optimal subproblems, we could define an even better “optimal” solution.

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A Characterizing Equation The global optimal has to be defined in terms of optimal subproblems, depending on where the final multiply is at. Let us consider all possible places for that final multiply: Recall that A i is a d i × d i+1 dimensional matrix. So, a characterizing equation for N i,j is the following: Note that subproblems are not independent--the subproblems overlap.

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A Dynamic Programming Algorithm Since subproblems overlap, we don’t use recursion. Instead, we construct optimal subproblems “bottom-up.” N i,i ’s are easy, so start with them Then do length 2,3,… subproblems, and so on. Running time: O(n 3 ) Algorithm matrixChain(S): Input: sequence S of n matrices to be multiplied Output: number of operations in an optimal paranethization of S for i 1 to n-1 do N i,i 0 for b 1 to n-1 do for i 0 to n-b-1 do j i+b N i,j +infinity for k i to j-1 do N i,j min{N i,j, N i,k +N k+1,j +d i d k+1 d j+1 }

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A Dynamic Programming Algorithm Visualization The bottom-up construction fills in the N array by diagonals N i,j gets values from pervious entries in i-th row and j-th column Filling in each entry in the N table takes O(n) time. Total run time: O(n 3 ) Getting actual parenthesization can be done by remembering “k” for each N entry answer N … n-1 … j i

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The General Dynamic Programming Technique Applies to a problem that at first seems to require a lot of time (possibly exponential), provided we have: Simple subproblems: the subproblems can be defined in terms of a few variables, such as j, k, l, m, and so on. Subproblem optimality: the global optimum value can be defined in terms of optimal subproblems Subproblem overlap: the subproblems are not independent, but instead they overlap (hence, should be constructed bottom-up).

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The 0/1 Knapsack Problem Given: A set S of n items, with each item i having b i - a positive benefit w i - a positive weight Goal: Choose items with maximum total benefit but with weight at most W. If we are not allowed to take fractional amounts, then this is the 0/1 knapsack problem. In this case, we let T denote the set of items we take Objective: maximize Constraint:

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Given: A set S of n items, with each item i having b i - a positive benefit w i - a positive weight Goal: Choose items with maximum total benefit but with weight at most W. Example Weight: Benefit: in2 in 6 in2 in $20$3$6$25$80 Items: 9 in Solution: 5 (2 in) 3 (2 in) 1 (4 in) “knapsack”

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A 0/1 Knapsack Algorithm, First Attempt S k : Set of items numbered 1 to k. Define B[k] = best selection from S k. Problem: does not have subproblem optimality: Consider S={(3,2),(5,4),(8,5),(4,3),(10,9)} weight-benefit pairs Best for S 4 : Best for S 5 :

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A 0/1 Knapsack Algorithm, Second Attempt S k : Set of items numbered 1 to k. Define B[k,w] = best selection from S k with weight exactly equal to w Good news: this does have subproblem optimality: I.e., best subset of S k with weight exactly w is either the best subset of S k-1 w/ weight w or the best subset of S k-1 w/ weight w-w k plus item k.

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(Weight,Value): (4,6) (3,8) (5,10) (8,13) (7,15) B S S S S4 S5 How to tell which objects were used for each box? Runtime?

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The 0/1 Knapsack Algorithm Recall def. of B[k,w]: Since B[k,w] is defined in terms of B[k-1,*], we can reuse the same array Running time: O(nW). Not a polynomial-time algorithm if W is large This is a pseudo-polynomial time algorithm Algorithm 01Knapsack(S, W): Input: set S of items w/ benefit b i and weight w i ; max. weight W Output: benefit of best subset with weight at most W for w 0 to W do B[w] 0 for k 1 to n do for w W downto w k do if B[w-w k ]+b k > B[w] then B[w] B[w-w k ]+b k

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