The Set Covering Prob.. Application Suppose X represents a set of skills that are needed to solve a problem and that we have a given set of people available.

Slides:



Advertisements
Similar presentations
Math 3121 Abstract Algebra I
Advertisements

THE WELL ORDERING PROPERTY Definition: Let B be a set of integers. An integer m is called a least element of B if m is an element of B, and for every x.
A threshold of ln(n) for approximating set cover By Uriel Feige Lecturer: Ariel Procaccia.
If R = {(x,y)| y = 3x + 2}, then R -1 = (1) x = 3y + 2 (2) y = (x – 2)/3 (3) {(x,y)| y = 3x + 2} (4) {(x,y)| y = (x – 2)/3} (5) {(x,y)| y – 2 = 3x} (6)
Lecture & 6.6 Inclusion-Exclusion. 6.5 Inclusion-Exclusion A AB U It’s simply a matter of not over-counting the blue area in the intersection.
Knapsack Problem Section 7.6. Problem Suppose we have n items U={u 1,..u n }, that we would like to insert into a knapsack of size C. Each item u i has.
Main Menu Main Menu (Click on the topics below) Combinations Example Theorem Click on the picture.
Complexity ©D Moshkovitz 1 Approximation Algorithms Is Close Enough Good Enough?
EE 4271 VLSI Design1 Logic Synthesis. Starts from RTL description in HDL or Boolean expressions Outputs a standard cell netlist EE 4271 VLSI Design2.
Discrete Structure Li Tak Sing( 李德成 ) Lectures
Counting Techniques: Combinations
Allocation problems - The Hungarian Algorithm The Hungarian algorithm Step 1Reduce the array by both row and column subtractions Step 2Cover the zero elements.
Approximation Algorithms
CSE115/ENGR160 Discrete Mathematics 04/21/11 Ming-Hsuan Yang UC Merced 1.
Statistics Lecture 17.
Probably Approximately Correct Model (PAC)
Network Optimization Models: Maximum Flow Problems In this handout: The problem statement Solving by linear programming Augmenting path algorithm.
Experts and Boosting Algorithms. Experts: Motivation Given a set of experts –No prior information –No consistent behavior –Goal: Predict as the best expert.
1 The Greedy Method CSC401 – Analysis of Algorithms Lecture Notes 10 The Greedy Method Objectives Introduce the Greedy Method Use the greedy method to.
Called as the Interval Scheduling Problem. A simpler version of a class of scheduling problems. – Can add weights. – Can add multiple resources – Can ask.
Round and Approx: A technique for packing problems Nikhil Bansal (IBM Watson) Maxim Sviridenko (IBM Watson) Alberto Caprara (U. Bologna, Italy)
Approximation Algorithms
Introduction to Real Analysis Dr. Weihu Hong Clayton State University 8/21/2008.
1 Prune-and-Search Method 2012/10/30. A simple example: Binary search sorted sequence : (search 9) step 1  step 2  step 3  Binary search.
1 Combinatorial Algorithms Local Search. A local search algorithm starts with an arbitrary feasible solution to the problem, and then check if some small,
CSE 589 Part VI. Reading Skiena, Sections 5.5 and 6.8 CLR, chapter 37.
CSE 589 Part V One of the symptoms of an approaching nervous breakdown is the belief that one’s work is terribly important. Bertrand Russell.
MCS 312: NP Completeness and Approximation algorthms Instructor Neelima Gupta
Matroids, Secretary Problems, and Online Mechanisms Nicole Immorlica, Microsoft Research Joint work with Robert Kleinberg and Moshe Babaioff.
1 Approximation algorithms Algorithms and Networks 2015/2016 Hans L. Bodlaender Johan M. M. van Rooij TexPoint fonts used in EMF. Read the TexPoint manual.
Section The Pigeonhole Principle If a flock of 20 pigeons roosts in a set of 19 pigeonholes, one of the pigeonholes must have more than 1 pigeon.
Approximation Algorithms Department of Mathematics and Computer Science Drexel University.
Input-Dependent and Asymptotic Approximation. Summary -Approximation algorithm for graph coloring -Approximation algorithm for set cover -Asymptotic approximation.
Dr Nazir A. Zafar Advanced Algorithms Analysis and Design Advanced Algorithms Analysis and Design By Dr. Nazir Ahmad Zafar.
The Set-covering Problem Problem statement –given a finite set X and a family F of subsets where every element of X is contained in one of the subsets.
Approximating Set Cover
The Subset-sum Problem
Formal Language & Automata Theory
Mathematical Foundations of AI
Hard Problems Some problems are hard to solve.
ENGM 535 Optimization Assignment Problems.
Advanced Algorithms Analysis and Design
Chapter 5. Greedy Algorithms
Advanced Algorithms Analysis and Design
Chapter 3 The Real Numbers.
NAME: OLUWATOSIN UTHMAN ZUBAIR (145919) COURSE: NETWORK FLOW
Presented by Po-Chuan & Chen-Chen 2016/03/08
Multi - Way Number Partitioning
CS154, Lecture 16: More NP-Complete Problems; PCPs
Assignment Problem A balanced transportation problem in which
Flooding © 2018.
Xuan Guo Lab 5 Xuan Guo.
Coverage Approximation Algorithms
Exploratory Exercises
Approximation Algorithms
Hn is the number of moves to solve. n = 1, Hn = 1 n = 2, Hn = 3 n = 3, Hn = 7 Hn = 2Hn
Advanced Algorithms Analysis and Design
Problem Solving 4.
Lecture 43 Section 10.1 Wed, Apr 6, 2005
Transportation Problems
Permutations and Combinations
Advanced Algorithms Analysis and Design
CSE 6408 Advanced Algorithms.
Advanced Analysis of Algorithms
CS154, Lecture 16: More NP-Complete Problems; PCPs
Approximation Algorithms for the Selection of Robust Tag SNPs
CS 6310 Advanced Design and Analysis of Algorithms Rajani Pingili

Math 0332 Subsets Name ________________________
Discrete Mathematics CS 2610
Presentation transcript:

The Set Covering Prob.

Application Suppose X represents a set of skills that are needed to solve a problem and that we have a given set of people available to work on the problem. We wish to form a committee, containing as few people as possible, such that for every requisite skill in X, there is a member of the committee having that skill.

How good is Greedy-Set-Cover?  (n)=H(max{|S|: S  F}) Proof: –We assign a cost of 1 to each set selected. –We distributed this cost over the elements covered for the first time. –C*: the optimal set cover –C: the set cover returned by the algorithm –Let Si be the ith subset selected

How good is Greedy-Set-Cover?  (n)=H(max{|S|: S  F}) dth harmonic number, denoted by H(d). As a boundary condition, we define H(0)=0.

Proof At each step of the algorithm, 1 unit of cost is assigned. Each element is assigned a cost only once. By the two statements above, we have

The cost assigned to the optimal set cover This is because each x  X is in at least one set S  C*

The number of elements in any S  F remaining uncovered after S 1, S 2,…, S i have been selected by the algorithm.

1/(a+1)+1/(a+2)+…1/b  1/b+1/b+…+1/b

This is because Hn = ln n + O(1)