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Dynamic Programming

Dynamic Programming. Optimization Problems. In which a set of choices must be made in order to arrive at an optimal (min/max) solution, subject to some constraints. (There may be several solutions to achieve an optimal value.) Two common techniques: Dynamic Programming (global)

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Dynamic Programming

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  1. Dynamic Programming Z. Guo

  2. Optimization Problems • In which a set of choices must be made in order to arrive at an optimal (min/max) solution, subject to some constraints. (There may be several solutions to achieve an optimal value.) • Two common techniques: • Dynamic Programming (global) • Greedy Algorithms (local)

  3. Dynamic Programming • Dynamic Programming is an algorithm design technique for optimization problems: often minimizing or maximizing. • Like divide and conquer, DP solves problems by combining solutions to subproblems. • Unlike divide and conquer, subproblems may overlap. • Subproblems may share subsubproblems, • However, solution to one subproblem may not affect the solutions to other subproblems of the same problem. (More on this later.) • DP reduces computation by • Solving subproblems in a bottom-up fashion. • Storing solution to a subproblem the first time it is solved. • Looking up the solution when subproblem is encountered again. • Key: determine structure of optimal solutions

  4. Steps in Dynamic Programming • Characterize structure of an optimal solution. • Define value of optimal solution recursively. • Compute optimal solution values either top-down with caching or bottom-up in a table. • Construct an optimal solution from computed values. We’ll study these with the help of two examples. Matrix MultiplicationLongest Common Subsequence

  5. Matrix Multiplication • In particular for 1  i p and 1  j  r, C[i, j] = k = 1 to qA[i, k]B[k, j] • Observe that there are pr total entries in C and each takes O(q) time to compute, thus the total time to multiply 2 matrices is pqr.

  6. Chain Matrix Multiplication • Given a sequence of matrices A1 A2…An , and dimensions p0 p1…pn where Ai is of dimension pi-1 x pi , determine multiplication sequence that minimizes the number of operations. • This algorithm does not perform the multiplication, it just figures out the best order in which to perform the multiplication.

  7. Example: CMM • Consider 3 matrices: A1be 5 x 4, A2be 4 x 6, and A3 be 6 x 2. Mult[((A1 A2)A3)] = (5x4x6) + (5x6x2) = 180 Mult[(A1 (A2A3 ))] = (4x6x2) + (5x4x2) = 88 Even for this small example, considerable savings can be achieved by reordering the evaluation sequence.

  8. Naive Algorithm • If we have just 1 item, then there is only one way to parenthesize. If we have n items, then there are n-1 places where you could break the list with the outermost pair of parentheses, namely just after the first item, just after the 2nd item, etc. and just after the (n-1)th item. • When we split just after the kth item, we create two sub-lists to be parenthesized, one with k items and the other with n-k items. Then we consider all ways of parenthesizing these. If there are Lways to parenthesize the left sub-list, R ways to parenthesize the right sub-list, then the total possibilities is LR.

  9. Cost of Naive Algorithm • The number of different ways of parenthesizing n items is P(n) = 1, if n = 1 P(n) = k = 1 to n-1P(k)P(n-k), if n  2 • This is related to Catalan numbers (which in turn is related to the number of different binary trees on n nodes). Specifically P(n) = C(n-1). C(n) = (1/(n+1))C(2n, n)  (4n / n3/2) where C(2n, n) stands for the number of various ways to choosenitems out of2nitems total.

  10. DP Solution (I) • Let Ai…j be the product of matrices i through j. Ai…j is a pi-1 x pj matrix. At the highest level, we are multiplying two matrices together. That is, for any k, 1  k  n-1, A1…n = (A1…k)(Ak+1…n) • The problem of determining the optimal sequence of multiplication is broken up into 2 parts: • : How do we decide where to split the chain (what k)? A : Consider all possible values of k. • : How do we parenthesize the subchains A1…k & Ak+1…n? A : Solve by recursively applying the same scheme. NOTE: this problem satisfies the “principle of optimality”. • Next, we store the solutions to the sub-problems in a table and build the table in a bottom-up manner.

  11. DP Solution (II) • For 1 i  j n, let m[i, j] denote the minimum number of multiplications needed to compute Ai…j. • Example: Minimum number of multiplies for A3…7 • In terms of pi , the product A3…7 has dimensions ____.

  12. DP Solution (III) • The optimal cost can be described be as follows: • i = j  the sequence contains only 1 matrix, so m[i, j]=0. • i < j  This can be split by considering each k, i  k < j, as Ai…k (pi-1 x pk ) times Ak+1…j (pk x pj). • This suggests the following recursive rule for computing m[i, j]: m[i, i] = 0 m[i, j] = mini  k < j(m[i, k] + m[k+1, j] + pi-1pkpj ) for i < j

  13. Computing m[i, j] • For a specific k,(Ai …Ak)(Ak+1…Aj) = m[i, j] = mini  k < j(m[i, k] + m[k+1, j] + pi-1pkpj )

  14. Computing m[i, j] • For a specific k,(Ai …Ak)(Ak+1…Aj) = Ai…k(Ak+1…Aj) (m[i, k] mults) m[i, j] = mini  k < j(m[i, k] + m[k+1, j] + pi-1pkpj )

  15. Computing m[i, j] • For a specific k,(Ai …Ak)(Ak+1…Aj) = Ai…k(Ak+1…Aj) (m[i, k] mults) = Ai…kAk+1…j(m[k+1, j] mults) m[i, j] = mini  k < j(m[i, k] + m[k+1, j] + pi-1pkpj )

  16. Computing m[i, j] • For a specific k,(Ai …Ak)(Ak+1…Aj) = Ai…k(Ak+1…Aj) (m[i, k] mults) = Ai…kAk+1…j(m[k+1, j] mults) = Ai…j(pi-1pk pjmults) m[i, j] = mini  k < j(m[i, k] + m[k+1, j] + pi-1pkpj )

  17. Computing m[i, j] • For a specific k,(Ai …Ak)(Ak+1…Aj) = Ai…k(Ak+1…Aj) (m[i, k] mults) = Ai…kAk+1…j(m[k+1, j] mults) = Ai…j(pi-1pk pjmults) • For solution, evaluate for all k and take minimum. m[i, j] = mini  k < j(m[i, k] + m[k+1, j] + pi-1pkpj )

  18. Matrix-Chain-Order(p) 1. n  length[p] - 1 2. for i  1 to n // initialization: O(n) time 3. do m[i, i]  0 4. for L  2 to n// L = length of sub-chain 5. dofor i  1 to n - L+1 6. do j  i + L - 1 7. m[i, j]   8.for k  i to j - 1 9. do q  m[i, k] + m[k+1, j] + pi-1 pk pj 10. if q < m[i, j] 11. then m[i, j]  q 12. s[i, j]  k 13. return m and s

  19. Example: DP for CMM • The initial set of dimensions are <5, 4, 6, 2, 7>: we are multiplying A1 (5x4) times A2(4x6) times A3 (6x2) times A4 (2x7). Optimal sequence is (A1 (A2A3 )) A4.

  20. Analysis • The array s[i, j] is used to extract the actual sequence (see example). • There are 3 nested loops and each can iterate at most n times, so the total running time is (n3).

  21. Extracting Optimum Sequence • Leave a split marker indicating where the best split is (i.e. the value of k leading to minimum values of m[i, j]). We maintain a parallel array s[i, j] in which we store the value of k providing the optimal split. • If s[i, j] = k, the best way to multiply the sub-chain Ai…j is to first multiply the sub-chain Ai…k and then the sub-chain Ak+1…j, and finally multiply them together. Intuitively s[i, j] tells us what multiplication to perform last. We only need to store s[i, j] if we have at least 2 matrices & j > i.

  22. Mult (A, i, j) 1. if (j > i) 2. then k = s[i, j] 3. X = Mult(A, i, k) // X = A[i]...A[k] 4. Y = Mult(A, k+1, j) // Y = A[k+1]...A[j] 5. return X*Y // Multiply X*Y 6. else returnA[i] // Return ith matrix

  23. Finding a Recursive Solution • Figure out the “top-level” choice you have to make (e.g., where to split the list of matrices) • List the options for that decision • Each option should require smaller sub-problems to be solved • Recursive function is the minimum (or max) over all the options m[i, j] = mini  k < j(m[i, k] + m[k+1, j] + pi-1pkpj )

  24. Longest Common Subsequence • Problem:Given 2 sequences, X = x1,...,xm and Y = y1,...,yn, find a common subsequence whose length is maximum. springtime ncaa tournament basketball printing north carolina Zhishan Subsequence need not be consecutive, but must be in order.

  25. Naïve Algorithm • For every subsequence of X, check whether it’s a subsequence of Y . • Time:Θ(n2m). • 2msubsequences of X to check. • Each subsequence takes Θ(n)time to check: scan Y for first letter, for second, and so on.

  26. Optimal Substructure Theorem Let Z = z1, . . . , zk be any LCS of X and Y . 1. If xm= yn, then zk= xm= ynand Zk-1 is an LCS of Xm-1 and Yn-1. 2. If xmyn, then either zkxmand Z is an LCS of Xm-1 and Y . 3. or zkynand Z is an LCS of X and Yn-1. Notation: prefix Xi= x1,...,xiis the first i letters of X. This says what any longest common subsequence must look like; do you believe it?

  27. Optimal Substructure Theorem Let Z = z1, . . . , zk be any LCS of X and Y . 1. If xm= yn, then zk= xm= ynand Zk-1 is an LCS of Xm-1 and Yn-1. 2. If xmyn, then either zkxmand Z is an LCS of Xm-1 and Y . 3. or zkynand Z is an LCS of X and Yn-1. Proof: (case 1: xm= yn) Any sequence Z’ that does not end in xm= yncan be made longer by adding xm= ynto the end. Therefore, • longest common subsequence (LCS) Z must end in xm= yn. • Zk-1 is a common subsequence of Xm-1 and Yn-1, and • there is no longer CS of Xm-1 and Yn-1, or Z would not be an LCS.

  28. Optimal Substructure Theorem Let Z = z1, . . . , zk be any LCS of X and Y . 1. If xm= yn, then zk= xm= ynand Zk-1 is an LCS of Xm-1 and Yn-1. 2. If xmyn, then either zkxmand Z is an LCS of Xm-1 and Y . 3. or zkynand Z is an LCS of X and Yn-1. Proof: (case 2: xmyn, andzkxm) Since Z does not end in xm, • Z is a common subsequence of Xm-1 and Y, and • there is no longer CS of Xm-1 and Y, or Z would not be an LCS.

  29. Recursive Solution • Define c[i, j] = length of LCS of Xiand Yj. • We want c[m,n]. This gives a recursive algorithm and solves the problem.But does it solve it well?

  30. Recursive Solution c[springtime, printing] c[springtim, printing] c[springtime, printin] [springti, printing] [springtim, printin] [springtim, printin] [springtime, printi] [springt, printing] [springti, printin] [springtim, printi] [springtime, print]

  31. Recursive Solution • Keep track of c[a,b] in a table of nm entries: • top/down • bottom/up

  32. Computing the length of an LCS LCS-LENGTH (X, Y) • m← length[X] • n← length[Y] • for i ← 1 to m • do c[i, 0] ← 0 • for j ← 0 to n • do c[0, j ] ← 0 • for i ← 1 to m • do for j ← 1 to n • do if xi= yj • then c[i, j ] ← c[i1, j1] + 1 • b[i, j ] ← “ ” • else if c[i1, j ] ≥ c[i, j1] • then c[i, j ] ← c[i 1, j ] • b[i, j ] ← “↑” • else c[i, j ] ← c[i, j1] • b[i, j ] ← “←” • return c and b b[i, j ] points to table entry whose subproblem we used in solving LCS of Xi and Yj. c[m,n] contains the length of an LCS of X and Y. Time:O(mn)

  33. Constructing an LCS PRINT-LCS (b, X, i, j) • if i = 0 or j = 0 • then return • if b[i, j ] = “ ” • then PRINT-LCS(b, X, i1, j1) • print xi • elseif b[i, j ] = “↑” • then PRINT-LCS(b, X, i1, j) • else PRINT-LCS(b, X, i, j1) • Initial call is PRINT-LCS (b, X,m, n). • When b[i, j ] = , we have extended LCS by one character. So LCS = entries with in them. • Time: O(m+n)

  34. Elements of Dynamic Programming • Optimal substructure • Overlapping subproblems

  35. Optimal Substructure • Show that a solution to a problem consists of making a choice, which leaves one or more subproblems to solve. • Suppose that you are given this last choice that leads to an optimal solution. • Given this choice, determine which subproblems arise and how to characterize the resulting space of subproblems. • Show that the solutions to the subproblems used within the optimal solution must themselves be optimal. Usually use cut-and-paste. • Need to ensure that a wide enough range of choices and subproblems are considered.

  36. Optimal Substructure • Optimal substructure varies across problem domains: • 1. How many subproblemsare used in an optimal solution. • 2. How many choices in determining which subproblem(s) to use. • Informally, running time depends on (# of subproblems overall)  (# of choices). • How many subproblems and choices do the examples considered contain? • Dynamic programming uses optimal substructure bottom up. • Firstfind optimal solutions to subproblems. • Thenchoose which to use in optimal solution to the problem.

  37. Overlapping Subproblems • The space of subproblems must be “small”. • The total number of distinct subproblems is a polynomial in the input size. • A recursive algorithm is exponential because it solves the same problems repeatedly. • If divide-and-conquer is applicable, then each problem solved will be brand new.

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