Dynamic Programming Louis Siu

What is Dynamic Programming (DP)? Not a single algorithm A technique for speeding up algorithms (making use of memory) Closely related to recursion

Why learn DP? DP problems appear in most, if not all, programming contests. Learning some common ways of making use of DP is easy but often sufficient. DP programs are usually easy to code. Good at DP => good at recursion

Dynamic Programming – Idea Fibonacci number f(n): f(0) = 0 f(1) = 1 f(n) = f(n – 1) + f(n – 2), n > 1 Code segment for finding f(n): int f(n){ if (n == 0) return 0; if (n == 1) return 1; return f(n-1) + f(n-2); }

Computing f(4) f(4) f(3)f(2) f(1) f(0) f(1)f(0) f(0), f(1), f(2) are computed multiple times! Exponential time for finding f(n)!

Modification Memorize computed result: int table[n]; int f(n){ if (table[n] is filled) return table[n]; if (n == 0) return 0; if (n == 1) return 1; table[n] = f(n-1) + f(n-2); return table[n]; }

Computing f(4) f(4) f(3)f(2) f(1) f(0) f(1)f(0) f(i) are computed once only. Linear time for finding f(n)!

Dynamic Programming – Idea Look for redundancy (re-computation) in the algorithm Memorize intermediate results

Example 1 – Tiling

Tiling (UVa #10359) Given a board of size 2×n Available tiles: 2×1, 2×2 Find the number of ways to completely fill the board. E.g. when n = 3, 5 ways

Recurrence Consider board of width n: How can we fill the right-most column? n

Recurrence Let f(n) be the number of ways of filling a board of size n × 2. f(n) = f(n–1) + f(n–2) + f(n–2) f(n–1) ways to fill f(n–2) ways to fill

Base Cases When n = 0, f(n) = 1. When n = 1, f(n) = 1.

Overlapping Subproblems Memorization can avoid re-computating f(n–3). f(n – 3) ways to fill

Implementation 1 Bottom-up approach: int ways[251]; int main(){ ways[0] = 1; ways[1] = 1; for (int i = 2; i <= 250; ++i) ways[i] = ways[i-1] + 2 * ways[i-2]; }

Implementation 2 Top-down approach: int ways[251]; int f(int n){ if (ways[n] != -1) return ways[n]; return (ways[n] = f(n-1) + 2*f(n-2)); } int main(){ ways[0] = 1; ways[1] = 1; for (int i = 2; i <= 250; ++i) ways[i] = -1; // cout << f(250) << ‘\n’; }

Bottom-up vs. Top-down Bottom-upTop-down Order of evaluation ImportantNot important Function callsNoYes Space requirement LowHigh RedundantPossibleNever

Remarks The number of solutions is Therefore, any algorithm that counts the number of solutions one by one is exponential. With memorization, f(i) is computed once only in constant time – the time complexity of our DP solution is O(n). Solve “Tiling” with big integer.

Example 2 – Tri Tiling

Tri Tiling (UVa #10918) Given a board of size 3×n Available tiles: 2×1 Find the number of ways to completely fill the board. Let f(n) be the number of ways to fill a board of size n×3 What is the relationship between f(n) and f(n–1), f(n–2), f(n–3), …?

Example The following way of filling the board cannot be counted using f(n–1), f(n–2), f(n–3), …

Recurrence How to fill the last column? g(n) is the number of ways to fill a board of size n×3 with one square at the corner filled f(n) = 2 × g(n–1) + f(n–2), f(0) = 1, f(1) = 0 g(n–1) ways to fill f(n–2) ways to fill

Recurrence for g(n) How to fill the last column? g(n) = f(n–1) + g(n–2), g(1) = 1, g(2) = 0 f(n–1) ways to fill g(n–2) ways to fill

Remarks Brute-force algorithm requires exponential time to finish. With memorization, f(i) and g(i) are computed once only in constant time – the time complexity of our DP solution is O(n). g(n) is the key for formulating the recurrence.

Example 3 – Little Stone Road (modified)

Little Stone Road (mini- contest) Input: an n×m grid with positive integers c(i, j) Output: the minimum cost of any path from any square in the top row to any square in the bottom row (can move down, left, right) E.g. minimum cost =

Attempt 1 Observations Many paths overlap significantly There are only 3 possible incoming squares that lead to square (i, j) – top, left and right f(i, j) = the min. cost of any path from the top to square (i, j) E.g. f(1, 1) = 16 (green path) f(i, j) = min { f(i-1, j), f(i, j-1), f(i, j+1) } + c(i, j) Base cases: f(0, j) = c(0, j) Answer = min { f(n-1, 0), f(n-1, 1), …, f(n-1, m-1) } Problems?

Attempt 1 – Problems f(i, j) = min { f(i-1, j), f(i, j-1), f(i, j+1) } + c(i, j) Base cases: f(0, j) = c(0, j) f(i, j) queries f(i, j±1), but they are problems of the same size – no progress towards the base case.

Attempt 2 Observation: The paths that ends at square (i, j) can be can be classified into n groups: f(i, j) = min { f(i-1, 0) + c(i, 0) + c(i, 1) + … + c(i, j), f(i-1, 1) + c(i, 1) + c(i, 2) + … + c(i, j), f(i-1, 2) + c(i, 3) + c(i, 4) + … + c(i, j), … f(i-1, j) + c(i, j), f(i-1, j+1) + c(i, j) + c(i, j+1), … } Base cases: f(0, j) = c(0, j) Time complexity: O(mn 2 ) (i, j)

Attempt 3 Observation: the cost of horizontal moves are calculated many times Let L(i, j) = minimum cost to square (i, j) “from the left or from above”, similarly for R(i, j) L(i, j) = min { L(i-1, j), R(i-1, j), L(i, j-1) } + c(i, j) R(i, j) = min { L(i-1, j), R(i-1, j), R(i, j+1) } + c(i, j) Answer = min { L(n-1, 0), L(n-1, 1), …, L(n-1, m-1), R(n-1, 0), R(n-1, 1), …, R(n-1, m-1) } Fill in the base cases yourself! Time complexity: O(mn) (i, j–1)(i, j)

Summary Recurrence must reduce big problems to small problems. There can be more than one DP formulation, with different performance.

Example 4 – Make Palindrome

Make Palindrome (UVa #10453) A palindrome is a string that is not changed when reversed. Example of palindrome: MADAM Example of non-palindrome: ADAM By inserting ‘M’ to “ADAM”, we get the palindrome “MADAM”. Find the minimum number of insertions required to make a string a palindrome. Show one palindrome produced using the minimum number of insertions.

Idea Let S[1..|S|] be the string. Observation: if S[1] and S[|S|] do not match, then an insertion either at the beginning or at the end is required. E.g. We MUST add ‘M’ at the front or ‘A’ at the end to make “ABCM” closer to a palindrome (“MABCM” or “ABCMA”) The problem remaining is to make “ABC” or “BCM” a palindrome.

Algorithm f(i, j) = minimum number of insertions required to make S[i, j] a palindrome. int table[1000][1000]; int f(i, j){ if (i >= j) return 0; if (table[i][j] is computed) return table[i][j]; if (S[i] == S[j]) return table[i][j] = f(i+1, j-1); return table[i][j] = min(f(i, j-1), f(i+1, j)) + 1; }

Backtrack – get back the resulting palindrome Let S = “ABCB” table[i][j]: min. # of insertions required to make S[i..j] a palindrome. table[i][j] = table[i+1][j-1] if S[i] = S[j] min{ table[i+1][j]+1, table[i][j-1]+1 }, otherwise jiji table[1][4] = table[2][4]+1 table[1][4] ≠ table[1][3]+1

int table[1000][1000]; string S; void backtrack(i, j){ if (i > j) return; if (i == j) cout << S[i]; else if (S[i] == S[j]){ cout << S[i]; backtrack(i+1, j-1); cout << S[j]; } else if (table[i+1][j]+1 == table[i][j]){ cout << S[i]; backtrack(i+1, j); cout << S[i]; } else { cout << S[j]; backtrack(i, j-1); cout << S[j]; }

Remarks Backtracking is done by checking how the values in the DP tables are derived. Further improvement: S = “ABAB” f(1, 2) is the same as f(3, 4), but they are computed separately. Store f(“A”), f(“B”), f(“AB”), f(“BA”), f(“ABA”), f(“BAB”), f(“ABAB”) instead. Need an efficient data structure to support this.

Example 5 – Traveling Salesman Problem

Traveling Salesman Problem (TSP) (UVa #10944) There are n cities. The costs of traveling between any pair of cities i and j, c(i, j), are given. The salesman wants to find the minimum cost to visit all cities exactly once and get back to the starting city. TSP is NP-complete.

Recurrence Let f(S, i) = the minimum cost to visit all cities in {1}  S  C, starting from city 1 and ending at city i, where C is the set of all cities. Answer = f(C, 1) Time complexity: O(2 n n 2 ) (much better than exhausting all (n-1)! possible circuits).

Summary and Conclusion DP is a powerful technique to improve running time of algorithms. The key idea of DP is avoiding re-computation by memorization. To make use of DP: identify redundancy define substructure (most difficult part) formulate recurrence Sometimes more than one DP formulations are possible. DP solutions can be implemented top-down or bottom-up. recover optimal solution by looking at how the entries in the DP table are derived. Alternatives to DP include brute-force with pruning and greedy. Practice is important.

Suggested Problems Easy: 116, 147, 231, 847, 926, 988, 10036, 10081, 10192, 10198, 10304, 10337, 10359, 10400, 10404, 10453, 10532, 10617, 10651, 10702, 10759, 10891, 10912, 10970, Medium: 562, 607, 711, 714, 882, 10003, 10130, 10944, 10890, 10981, 11002, 11003, Hard: 757, 10020, 10564, 10663, 10859, 10911, 10934, 10940, 11045, 11081