Download presentation

Presentation is loading. Please wait.

Published byRaquel Conte Modified over 6 years ago

1
David Luebke 1 5/4/2015 CS 332: Algorithms Dynamic Programming Greedy Algorithms

2
David Luebke 2 5/4/2015 Administrivia l Hand back midterm l Go over problem values

3
David Luebke 3 5/4/2015 Review: Amortized Analysis l To illustrate amortized analysis we examined dynamic tables 1. Init table size m = 1 2. Insert elements until number n > m 3. Generate new table of size 2m 4. Reinsert old elements into new table 5. (back to step 2) l What is the worst-case cost of an insert? l What is the amortized cost of an insert?

4
David Luebke 4 5/4/2015 Review: Analysis Of Dynamic Tables l Let c i = cost of ith insert l c i = i if i-1 is exact power of 2, 1 otherwise l Example: n OperationTable Size Cost Insert(1)11 1 Insert(2)21 + 1 2 Insert(3)41 + 2 Insert(4)41 Insert(5)81 + 4 Insert(6)81 Insert(7)81 Insert(8)81 Insert(9)161 + 8 1 2 3 4 5 6 7 8 9

5
David Luebke 5 5/4/2015 Review: Aggregate Analysis l n Insert() operations cost l Average cost of operation = (total cost)/(# operations) < 3 l Asymptotically, then, a dynamic table costs the same as a fixed-size table n Both O(1) per Insert operation

6
David Luebke 6 5/4/2015 Review: Accounting Analysis l Charge each operation $3 amortized cost n Use $1 to perform immediate Insert() n Store $2 l When table doubles n $1 reinserts old item, $1 reinserts another old item n We’ve paid these costs up front with the last n/2 Insert()s l Upshot: O(1) amortized cost per operation

7
David Luebke 7 5/4/2015 Review: Accounting Analysis l Suppose must support insert & delete, table should contract as well as expand n Table overflows double it (as before) n Table < 1/4 full halve it n Charge $3 for Insert (as before) n Charge $2 for Delete u Store extra $1 in emptied slot u Use later to pay to copy remaining items to new table when shrinking table l What if we halve size when table < 1/8 full?

8
David Luebke 8 5/4/2015 Review: Longest Common Subsequence l Longest common subsequence (LCS) problem: n Given two sequences x[1..m] and y[1..n], find the longest subsequence which occurs in both n Ex: x = {A B C B D A B }, y = {B D C A B A} n {B C} and {A A} are both subsequences of both u What is the LCS? n Brute-force algorithm: For every subsequence of x, check if it’s a subsequence of y u What will be the running time of the brute-force alg?

9
David Luebke 9 5/4/2015 LCS Algorithm l Brute-force algorithm: 2 m subsequences of x to check against n elements of y: O(n 2 m ) l But LCS problem has optimal substructure n Subproblems: pairs of prefixes of x and y l Simplify: just worry about LCS length for now n Define c[i,j] = length of LCS of x[1..i], y[1..j] n So c[m,n] = length of LCS of x and y

10
David Luebke 10 5/4/2015 Finding LCS Length l Define c[i,j] = length of LCS of x[1..i], y[1..j] l Theorem: l What is this really saying?

11
David Luebke 11 5/4/2015 Optimal Substructure of LCS l Observation 1: Optimal substructure n A simple recursive algorithm will suffice n Draw sample recursion tree from c[3,4] n What will be the depth of the tree? l Observation 2: Overlapping subproblems n Find some places where we solve the same subproblem more than once

12
David Luebke 12 5/4/2015 Structure of Subproblems l For the LCS problem: n There are few subproblems in total n And many recurring instances of each (unlike divide & conquer, where subproblems unique) l How many distinct problems exist for the LCS of x[1..m] and y[1..n]? l A: mn

13
David Luebke 13 5/4/2015 Memoization l Memoization is one way to deal with overlapping subproblems n After computing the solution to a subproblem, store in a table n Subsequent calls just do a table lookup l Can modify recursive alg to use memoziation: n There are mn subproblems n How many times is each subproblem wanted? n What will be the running time for this algorithm? The running space?

14
David Luebke 14 5/4/2015 Dynamic Programming l Dynamic programming: build table bottom-up n Same table as memoization, but instead of starting at (m,n) and recursing down, start at (1,1) n Draw LCS-length table for i=0..7, j=0..6: u X (vert) = {A B C B D A B}, Y (horiz) = {B D C A B A} u Initialize top row/left column to 0, march across rows u What values does a given cell depend on? n What is the final length of the LCS? the LCS itself? n What is the running time? space? l Can actually reduce space to O(min(m,n))

15
David Luebke 15 5/4/2015 Dynamic Programming l Summary of the basic idea: n Optimal substructure: optimal solution to problem consists of optimal solutions to subproblems n Overlapping subproblems: few subproblems in total, many recurring instances of each n Solve bottom-up, building a table of solved subproblems that are used to solve larger ones l Variations: n “Table” could be 3-dimensional, triangular, a tree, etc.

16
David Luebke 16 5/4/2015 Greedy Algorithms l A greedy algorithm always makes the choice that looks best at the moment n The hope: a locally optimal choice will lead to a globally optimal solution n For some problems, it works n My example: walking to the Corner l Dynamic programming can be overkill; greedy algorithms tend to be easier to code

17
David Luebke 17 5/4/2015 Activity-Selection Problem l Problem: get your money’s worth out of a carnival n Buy a wristband that lets you onto any ride n Lots of rides, each starting and ending at different times n Your goal: ride as many rides as possible u Another, alternative goal that we don’t solve here: maximize time spent on rides l Welcome to the activity selection problem

18
David Luebke 18 5/4/2015 Activity-Selection l Formally: n Given a set S of n activities s i = start time of activity i f i = finish time of activity i n Find max-size subset A of compatible activities n Assume (wlog) that f 1 f 2 … f n 1 2 3 4 5 6

19
David Luebke 19 5/4/2015 Activity Selection: Optimal Substructure l Let k be the minimum activity in A (i.e., the one with the earliest finish time). Then A - {k} is an optimal solution to S’ = {i S: s i f k } n In words: once activity #1 is selected, the problem reduces to finding an optimal solution for activity- selection over activities in S compatible with #1 n Proof: if we could find optimal solution B’ to S’ with |B| > |A - {k}|, u Then B {k} is compatible u And |B {k}| > |A|

20
David Luebke 20 5/4/2015 Activity Selection: Repeated Subproblems l Consider a recursive algorithm that tries all possible compatible subsets to find a maximal set, and notice repeated subproblems: S 1 A? S’ 2 A? S-{1} 2 A? S-{1,2}S’’S’-{2}S’’ yesno yes

21
David Luebke 21 5/4/2015 Greedy Choice Property l Dynamic programming? Memoize? Yes, but… l Activity selection problem also exhibits the greedy choice property: n Locally optimal choice globally optimal sol’n n Them 17.1: if S is an activity selection problem sorted by finish time, then optimal solution A S such that {1} A u Sketch of proof: if optimal solution B that does not contain {1}, can always replace first activity in B with {1} (Why?). Same number of activities, thus optimal.

22
David Luebke 22 5/4/2015 Activity Selection: A Greedy Algorithm l So actual algorithm is simple: n Sort the activities by finish time n Schedule the first activity n Then schedule the next activity in sorted list which starts after previous activity finishes n Repeat until no more activities l Intuition is even more simple: n Always pick the shortest ride available at the time

23
David Luebke 23 5/4/2015 The End

Similar presentations

© 2022 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google