版权说明:本文档由用户提供并上传,收益归属内容提供方,若内容存在侵权,请进行举报或认领
文档简介
1、Chapter 3 The GREEDY METHODThe design and Analysis of Computer AlgorithmsOutlineThe greedy method is a strategy to solve some optimization problems. It employs the following approach: In each stage, our decision is a locally-optimal one. Only a few optimization problems can be solved by this greedy
2、method.To solve some problems using this greedy method. The greedy method Suppose that a problem can be solved by a sequence of decisions. The greedy method has that each decision is locally optimal. These locally optimal solutions will finally add up to a globally optimal solution.Only a few optimi
3、zation problems can be solved by the greedy method. An simple exampleProblem: Pick k numbers out of n numbers such that the sum of these k numbers is the largest.Algorithm:FOR i = 1 to kpick out the largest number and delete this number from the input.ENDFORShortest paths on a special graphProblem:
4、Find a shortest path from v0 to v3.The greedy method can solve this problem.The shortest path: 1 + 2 + 4 = 7.Shortest paths on a multi-stage graphProblem: Find a shortest path from v0 to v3 in the multi-stage graph.Greedy method: v0v1,2v2,1v3 = 23Optimal: v0v1,1v2,2v3 = 7The greedy method does not w
5、ork.Solution of the above problem dmin(i,j): minimum distance between i and j. This problem can be solved by the dynamic programming method. Minimum spanning trees (MST) It may be defined on Euclidean space points or on a graph.G = (V, E): weighted connected undirected graph Spanning tree : S = (V,
6、T), T E, undirected treeMinimum spanning tree(MST) : a spanning tree with the smallest total weight. The vertices set of spanning tree is the same as the vertices set of Graph G!An example of MSTA graph and one of its minimum costs spanning treeKruskals algorithm for finding MSTStep 1: Sort all edge
7、s into nondecreasing order. Step 2: Add the next smallest weight edge to the forest if it will not cause a cycle.Step 3: Stop if n-1 edges. Otherwise, go to Step2.An example of Kruskals algorithm(1,2) - 10 (3,5) - 35(3,6) - 15 (2,5) - 40(4,6) - 20 (1,5) - 45(2,6) - 25 (2,3) - 50(1,4) - 30 (5,6) - 55
8、The details for constructing MSTHow do we check if a cycle is formed when a new edge is added?By the SET and UNION method.A tree in the forest is used to represent a SET.If (u, v) E and u, v are in the same set, then the addition of (u, v) will form a cycle.If (u, v) E and uS1 , vS2 , then perform U
9、NION of S1 and S2 .Time complexityTime complexity: O(|E| log|E|)Step 1: O(|E| log|E|)Step 2 & Step 3: Where is the inverse of Ackermanns function.Ackermanns function A(p, q+1) A(p, q), A(p+1, q) A(p, q)65536 twosInverse of Ackermanns function (m, n) = minZ1|A(Z,4m/n) log2n Practically, A(3,4) log2n
10、(m, n) 3 (m, n) is almost a constant.Prims algorithm for finding MSTStep 1: x V, Let A = x, B = V - x.Step 2: Select (u, v) E, u A, v B such that (u, v) has the smallest weight between A and B.Step 3: Put (u, v) in the tree. A = A v, B = B - vStep 4: If B = , stop; otherwise, go to Step 2. Time comp
11、lexity : O(n2), n = |V|. (see the example on the next page)An example for Prims algorithm(1,2) - 10 (3,5) - 35(3,6) - 15 (2,5) - 40(4,6) - 20 (1,5) - 45(2,6) - 25 (2,3) - 50(1,4) - 30 (5,6) - 55 The set A 1, 2 1, 2, 6 1, 2, 6, 3 1, 2, 6, 3, 4 1, 2, 6, 3, 4, 5An example for Prims algorithmThe set B=V
12、-A 3, 4, 5, 6 3, 4, 5 4, 5 5 The set V (Include all vertices): 1, 2, 3, 4, 5, 6Step 1Step 2Step 3Step 4Step 5V(V)Comparison of Kruskals and Prims algorithmsTo get the Minimum spanning trees (MST) using Kruskals algorithmsTo get the Minimum spanning trees (MST) using Prims algorithmsThe single-source
13、 shortest path problem shortest paths from v0 to all destinationsDirected graphTo get the shortest paths from source V0 to all destinationsDirected graphDr. Yan Dijkstras algorithmCost adjacency matrix. All entries not shown are +. Time complexity : O(n2)Can we use Dijkstras algorithm to find the lo
14、ngest path from a starting vertex to an ending vertex in an acyclic directed graph?There are 3 possible ways to apply Dijkstras algorithm:Directly use “max” operations instead of “min” operations.Convert all positive weights to be negative. Then find the shortest path.Give a very large positive numb
15、er M. If the weight of an edge is w, now M-w is used to replace w. Then find the shortest path. All these 3 possible ways would not work!The longest path problemActivity On Edge (AOE) NetworksTasks (activities) : a0, a1,Events : v0,v1,Some definition:PredecessorSuccessorImmediate predecessorImmediat
16、e successorcritical pathA critical path is a path that has the longest length. (v0, v1, v4, v7, v8)6 + 1 + 7 + 4 = 18 (Max)The earliest timeThe earliest time of an activity, ai, can occur is the length of the longest path from the start vertex v0 to ais start vertex.(Ex: the earliest time of activit
17、y a7 can occur is 7.)We denote this time as early(i) for activity ai. early(6) = early(7) = 7.The latest timeThe latest time, late(i), of activity, ai, is defined to be the latest time the activity may start without increasing the project duration.Ex: early(5) = 5 & late(5) = 8; early(7) = 7 & late(
18、7) = 7late(5) = 18 4 4 - 2 = 8late(7) = 18 4 7 = 7Critical activityA critical activity is an activity for which early(i) = late(i).The difference between late(i) and early(i) is a measure of how critical an activity is.Calculation of Earliest TimesLet activity ai is represented by edge (u, v).early
19、(i) = earliest ulate (i) = latest v duration of activity aiWe compute the times in two stages:a forward stage and a backward stage.The forward stage:Step 1: earliest 0 = 0Step 2: earliest j = max earliest i + duration of (i, j)i is in P(j)P(j) is the set of immediate predecessors of j.The backward s
20、tage:Step 1: latestn-1 = earliestn-1Step 2: latest j = min latest i - duration of (j, i) i is in S(j)S(j) is the set of vertices adjacent from vertex j.latest8 = earliest8 = 18latest6 = minearliest8 - 2 = 16latest7 = minearliest8 - 4 = 14latest4 = minearliest6 9; earliest7 7 = 7latest1 = minearliest
21、4 - 1 = 6latest2 = minearliest4 - 1 = 6latest5 = minearliest7 - 4 = 10latest3 = minearliest5 - 2 = 8latest0 = minearliest1 6; earliest2 4; earliest3 5 = 0Calculation of Latest TimesGraph with non-critical activities deletedActivityEarlyLateL - ECriticala0000Yesa1022Noa2033Noa3660Yesa4462Noa5583Noa67
22、70Yesa7770Yesa87103Noa916160Yesa1014140YesThe longest path(critical path) problem can be solved by the critical path method(CPM) :Step 1:Find a topological ordering.Step 2: Find the critical path. (see Horiwitz 1998.)CPM for the longest path problemThe 2-way merging problem # of comparisons required
23、 for the linear 2-way merge algorithm is m1+ m2 -1 where m1 and m2 are the lengths of the two sorted lists respectively.The problem: There are n sorted lists, each of length mi. What is the optimal sequence of merging process to merge these n lists into one sorted list ?Extended Binary Tree Represen
24、ting a 2-way MergeExtended binary treesAn example of 2-way merging Example: 6 sorted lists with lengths 2, 3, 5, 7, 11 and 13.Time complexity for generating an optimal extended binary tree:O(n log n)Huffman codes In telecommunication, how do we represent a set of messages, each with an access freque
25、ncy, by a sequence of 0s and 1s?To minimize the transmission and decoding costs, we may use short strings to represent more frequently used messages.This problem can be solved by using an extended binary tree which is used in the 2-way merging problem.5. Huffman codesIn principle Huffman codes can b
26、e used for a widevariety of compression tasks, but as progress has been made in the theory and practice of compression technology, newer approaches to compression have supplanted their use. However, the mathematical ideas behind Huffman codes remain as fresh and exciting as when they were first deve
27、loped. Huffman developed his ideas at MIT where he was working under the supervision of Robert Fano. Imagine that we have a text or image. For a text assume we know in advance exactly how many times each character (i.e. lower case or upper case letter, space, punctuation symbol) appears. In the case
28、 of an image, we would assume that the gray levels of the pixels in the image are all known in advance. In either case we know the relative frequencies (probabilities) of the items that we must develop a compression code for. The algorithm for constructing a Huffman code to represent items with give
29、n relative frequencies (probabilities) of occurrence proceeds in two phases. First, one constructs a Huffman tree based on the relative frequencies. Second, using the Huffman tree, one constructs the code words that go with the given relative frequencies. A simple example will illustrate the lovely
30、ideas involved.An example of Huffman algorithmSymbols: A, B, C, D, E, F, G freq. : 2, 3, 5, 8, 13, 15, 18Huffman codes:A: 10100B: 10101 C: 1011 D: 100 E: 00 F: 01G: 11Huffman codesGreedy methodInput(A1n)Solution for i 1 to n doX SELECT(A)If FEASIBLE( solution, x) then solution UNION( select, x)endif
31、repeatOutput (solution)(1)To make a group of decision(2)Every decision is only responsible for its own optimization(3) Local optimal must be global optimal(4)This process implicit some actions such as: sorting, etc. Knapsack problemGiven positive integers P1, P2, , Pn,W1, W2, , Wn and M.Find X1, X2,
32、 ,Xn, 0Xi1 such that is maximized.Subject to Knapsack Problem ExampleM = 20, (P1, P2, P3)=(25,24,15) (W1, W2, W3) = (18, 15, 10)Four feasible solutions, 4 is optimal(X1, X2, X3)WiXiPiX1.(1/2,1/3,1/4)16.524.252.(1,2/15,0)2028.23.(0, 2/3, 1)20314.(0, 1, 1/2)2031.5How to select them according to optimal value?We should calculate the profit per weight P/W!Sort them into decreasing order. P1/W11.5 P3/W3=1.5 P2/W2 P3/W3 P1/W1Job Sequencing with DeadlinesGive
温馨提示
- 1. 本站所有资源如无特殊说明,都需要本地电脑安装OFFICE2007和PDF阅读器。图纸软件为CAD,CAXA,PROE,UG,SolidWorks等.压缩文件请下载最新的WinRAR软件解压。
- 2. 本站的文档不包含任何第三方提供的附件图纸等,如果需要附件,请联系上传者。文件的所有权益归上传用户所有。
- 3. 本站RAR压缩包中若带图纸,网页内容里面会有图纸预览,若没有图纸预览就没有图纸。
- 4. 未经权益所有人同意不得将文件中的内容挪作商业或盈利用途。
- 5. 人人文库网仅提供信息存储空间,仅对用户上传内容的表现方式做保护处理,对用户上传分享的文档内容本身不做任何修改或编辑,并不能对任何下载内容负责。
- 6. 下载文件中如有侵权或不适当内容,请与我们联系,我们立即纠正。
- 7. 本站不保证下载资源的准确性、安全性和完整性, 同时也不承担用户因使用这些下载资源对自己和他人造成任何形式的伤害或损失。
最新文档
- 《2024年 面向移动终端的目标检测系统的设计与实现》范文
- 税收理论与实务(第六版)课件 第三章 消费税
- 2023年台站测风仪项目评估分析报告
- 2024【合同范本】委托购销合同范本
- 2023年云物融合项目需求分析报告
- 2023年机动车零部件及配件项目调研分析报告
- 2024劳动合同,教学科研岗位聘任合同
- 2024年宾馆服务员合同的范本
- 2024年民间个人借款合同范本
- 有关租房协议书合同范本参考模板
- 新教师如何备课与上课PPT
- (完整word版)硬件研发管理办法
- 2016版江苏省住院病历质量评定标准
- 部编版六年级语文上册第三单元教材分析及全部教案
- 放射科自查表.doc
- 适用于老师做证婚人时的证婚词_教师做婚礼证婚人致辞婚礼致辞
- 关于编制经营计划与财务预算的通知
- 老挝电力技术规范(中文译稿)
- 部编人教版一年级上册语文《我爱学语文》教学课件
- 羽毛球场馆经营管理现状的调查与分析
- 专家论证全套表格(最新)
评论
0/150
提交评论