Polyhedral Optimization Lecture 5 – Part 2 M. Pawan Kumar Slides available online

Slides:



Advertisements
Similar presentations
C&O 355 Lecture 6 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A.
Advertisements

C&O 355 Lecture 23 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A A A A A A A.
1 LP Duality Lecture 13: Feb Min-Max Theorems In bipartite graph, Maximum matching = Minimum Vertex Cover In every graph, Maximum Flow = Minimum.
C&O 355 Mathematical Programming Fall 2010 Lecture 22 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A.
Totally Unimodular Matrices
Local and Global Optima
Complexity 16-1 Complexity Andrei Bulatov Non-Approximability.
Reducing the collection of itemsets: alternative representations and combinatorial problems.
The Submodular Welfare Problem Lecturer: Moran Feldman Based on “Optimal Approximation for the Submodular Welfare Problem in the Value Oracle Model” By.
Totally Unimodular Matrices Lecture 11: Feb 23 Simplex Algorithm Elliposid Algorithm.
1 Introduction to Linear and Integer Programming Lecture 9: Feb 14.
Duality Lecture 10: Feb 9. Min-Max theorems In bipartite graph, Maximum matching = Minimum Vertex Cover In every graph, Maximum Flow = Minimum Cut Both.
Duality Dual problem Duality Theorem Complementary Slackness
A general approximation technique for constrained forest problems Michael X. Goemans & David P. Williamson Presented by: Yonatan Elhanani & Yuval Cohen.
Tutorial 12 Linear programming Quadratic programming.
Computability and Complexity 24-1 Computability and Complexity Andrei Bulatov Approximation.
6. Linear Programming (Graphical Method) Objectives: 1.More than one solution 2.Unbounded feasible region 3.Examples Refs: B&Z 5.2.
Polyhedral Optimization Lecture 3 – Part 3 M. Pawan Kumar Slides available online
Martin Grötschel Institute of Mathematics, Technische Universität Berlin (TUB) DFG-Research Center “Mathematics for key technologies” (M ATHEON ) Konrad-Zuse-Zentrum.
1 Spanning Tree Polytope x1 x2 x3 Lecture 11: Feb 21.
Polyhedral Optimization Lecture 3 – Part 2
Polyhedral Optimization Lecture 1 – Part 1 M. Pawan Kumar Slides available online
Polyhedral Optimization Lecture 4 – Part 3 M. Pawan Kumar Slides available online
Mathematics Review Exponents Logarithms Series Modular arithmetic Proofs.
Polyhedral Optimization Lecture 5 – Part 1 M. Pawan Kumar Slides available online
C&O 355 Mathematical Programming Fall 2010 Lecture 4 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A.
Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. Sets.
Theory of Computing Lecture 13 MAS 714 Hartmut Klauck.
Martin Grötschel  Institute of Mathematics, Technische Universität Berlin (TUB)  DFG-Research Center “Mathematics for key technologies” (M ATHEON ) 
Competitive Queue Policies for Differentiated Services Seminar in Packet Networks1 Competitive Queue Policies for Differentiated Services William.
Polyhedral Optimization Lecture 4 – Part 2 M. Pawan Kumar Slides available online
Approximation Algorithms for Prize-Collecting Forest Problems with Submodular Penalty Functions Chaitanya Swamy University of Waterloo Joint work with.
Discrete Optimization Lecture 4 – Part 1 M. Pawan Kumar Slides available online
To prove by induction that 3 is a factor of 4 n - 1, n  N Next (c) Project Maths Development Team 2011.
1 System Planning 2013 Lecture 7: Optimization Appendix A Contents: –General about optimization –Formulating optimization problems –Linear Programming.
Optimization - Lecture 4, Part 1 M. Pawan Kumar Slides available online
X y x-y · 4 -y-2x · 5 -3x+y · 6 x+y · 3 Given x, for what values of y is (x,y) feasible? Need: y · 3x+6, y · -x+3, y ¸ -2x-5, and y ¸ x-4 Consider the.
Chapter 8 Maximum Flows: Additional Topics All-Pairs Minimum Value Cut Problem  Given an undirected network G, find minimum value cut for all.
Lecture.6. Table of Contents Lp –rounding Dual Fitting LP-Duality.
Earliness and Tardiness Penalties Chapter 5 Elements of Sequencing and Scheduling by Kenneth R. Baker Byung-Hyun Ha R1.
1 JOB SEQUENCING WITH DEADLINES The problem is stated as below. There are n jobs to be processed on a machine. Each job i has a deadline d i ≥ 0 and profit.
Slide Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley.
Martin Grötschel  Institute of Mathematics, Technische Universität Berlin (TUB)  DFG-Research Center “Mathematics for key technologies” (M ATHEON ) 
Discrete Optimization Lecture 5 – Part 1 M. Pawan Kumar Slides available online
Optimization - Lecture 5, Part 1 M. Pawan Kumar Slides available online
Submodularity Reading Group Submodular Function Minimization via Linear Programming M. Pawan Kumar
Submodularity Reading Group Matroids, Submodular Functions M. Pawan Kumar
Searching a Linear Subspace Lecture VI. Deriving Subspaces There are several ways to derive the nullspace matrix (or kernel matrix). ◦ The methodology.
Polyhedral Optimization Lecture 5 – Part 3 M. Pawan Kumar Slides available online
Discrete Optimization Lecture 3 – Part 1 M. Pawan Kumar Slides available online
Approximation Algorithms based on linear programming.
Unconstrained Submodular Maximization Moran Feldman The Open University of Israel Based On Maximizing Non-monotone Submodular Functions. Uriel Feige, Vahab.
Submodularity Reading Group Matroid Polytopes, Polymatroid M. Pawan Kumar
Submodularity Reading Group Matroids, Submodular Functions M. Pawan Kumar
Polyhedral Optimization Lecture 2 – Part 2 M. Pawan Kumar Slides available online
Lap Chi Lau we will only use slides 4 to 19
Chap 10. Sensitivity Analysis
Topics in Algorithms Lap Chi Lau.
Computational Optimization
Submodularity Reading Group Polymatroid
Set Topology MTH 251 Lecture # 8.
Chap 3. The simplex method
Analysis of Algorithms
3.5 Minimum Cuts in Undirected Graphs
Coverage Approximation Algorithms
I.4 Polyhedral Theory (NW)
Advanced LP models column generation.
V12 Menger’s theorem Borrowing terminology from operations research
I.4 Polyhedral Theory.
Submodular Maximization with Cardinality Constraints
Presentation transcript:

Polyhedral Optimization Lecture 5 – Part 2 M. Pawan Kumar Slides available online

(Extended) Polymatroid Optimization over Polymatroids Minimizing Submodular Functions Outline

Polymatroid Set S Real vector x of size |S|x1 Submodular function f P f = {x ≥ 0, x(U) ≤ f(U) for all U ⊆ S} EP f = {x(U) ≤ f(U) for all U ⊆ S} Polymatroid Extended Polymatroid

Tight Sets Set SSubmodular function f EP f = {x(U) ≤ f(U) for all U ⊆ S} U is tight with respect to x ∈ EP f if x(U) = f(U) Tight sets are closed under union Tight sets are closed under intersection Proof?

Proof Sketch Let T and U be tight wrt x ∈ EP f ≥ x(T ∪ U) + x(T ∩ U) f(T) + f(U) ≥ f(T ∪ U) + f(T ∩ U) = x(T) + x(U) = f(T) + f(U) All inequalities must be equalities

(Extended) Polymatroid Optimization over Polymatroids Minimizing Submodular Functions Outline

Primal Problem max w T x x ∈ EP f Assume f(null set) ≥ 0 Otherwise EP f is empty f(null set) can be set to 0Why? Decreasing f(null set) maintains submodularity

Primal Problem max w T x x ∈ EP f Assume w ≥ 0 Otherwise the optimal solution is infinityWhy? Let us first try to find a feasible solution

Greedy Algorithm max w T x x ∈ EP f Order s 1,s 2,…,s n ∈ S such that w(s i ) ≥ w(s i+1 ) Define U i = {s 1,s 2,..,s i } x G i = f(U i ) – f(U i-1 ) x G ∈ EP f Proof?

Proof Sketch We have to show that x G (T) ≤ f(T) for all T ⊆ S Trivial when T = null set Mathematical induction on |T|

Proof Sketch Let k be the largest index such that s k ∈ T Clearly |T| ≤ |U k | x G (T) = x G (T\{s k }) + x G k = f(T\{s k }) + f(U k ) - f(U k-1 ) ≤ f(T) ≤ f(T\{s k }) + x G k Why? Induction Why? Submodularity Definition

Dual Problem max w T x x ∈ EP f min ∑ T y T f(T) y T ≥ 0, for all T ⊆ S ∑ T y T v T = w Let us first try to find a feasible dual solution

Greedy Algorithm Order s 1,s 2,…,s n ∈ S such that w(s i ) ≥ w(s i+1 ) Define U i = {s 1,s 2,..,s i } y G U i = w(s i ) - w(s i+1 ) y G is feasible y G S = w(s n ) y G T = 0, for all other T Proof?

Proof Sketch Trivially, y G ≥ 0 Consider s i ∈ S ∑ T ∋ s i y G T = ∑ j≥i y G U j = w(s i ) ∑ T y T v T = w

Optimality Primal feasible solution x G Dual feasible solution y G Primal value at x G = Dual value at y G Proof?

Proof Sketch w T x G = ∑ s ∈ S w(s)x G s = ∑ i ∈ {1,2,…,n} w(s i )(f(U i ) - f(U i-1 )) = ∑ i ∈ {1,2,…,n-1} f(U i )(w(s i ) - w(s i+1 )) + f(S)w(s n ) = ∑ T y G T f(T)

Optimality Primal feasible solution x G Dual feasible solution y G Primal value at x G = Dual value at y G Therefore, x G is an optimal primal solution And, y G is an optimal dual solution

(Extended) Polymatroid Optimization over Polymatroids Minimizing Submodular Functions Outline

Submodular Function Minimization min T ⊆ S f(T) We will assume f(null set) = 0 If not, we can add a constant

Submodular Function Minimization min T ⊆ S f(T) Brute force search is exponential in |S| We will prove that SFM is easy First, we need two properties

Property 1 f(U) = max {x(U) | x ∈ EP f } Set w = v U Proof? f is a submodular function over S

Property 2 f is a submodular function over S Define f’(U) = min T ⊆ U f(T) f’ is also submodularProof?

Proof Sketch We have to prove the following f’(T) + f’(U) ≥ f’(T ∪ U) + f’(T ∩ U) for all T, U ⊆ S f’(T) = f(X) for some X ⊆ T f’(U) = f(Y) for some Y ⊆ S

Proof Sketch f’(T) + f’(U)= f(X) + f(Y) ≥ f(X ∪ Y) + f(X ∩ Y) ≥ f’(T ∪ U) + f’(T ∩ U)

Property 2 Continued f is a submodular function over S Define f’(U) = min T ⊆ U f(T) f’ is also submodular EP f’ = { x ∈ EP f, x ≤ 0} Proof?

Proof Sketch If x ∈ P, then x(U) ≤ f(U) for all U ⊆ S x(T\U) + x(U) ≤ f(U), for all U ⊆ T ⊆ S Why? Because x ∈ EP f Why? Because x ≤ 0 We can show that { x ∈ EP f, x ≤ 0} = P ⊆ EP f’

Proof Sketch We can show that { x ∈ EP f, x ≤ 0} = P ⊇ EP f’ If x ∈ EP f’ then x ∈ EP f For any s ∈ S, x s ≤ 0 Why? Because x(U) ≤ f(U) for all U ⊆ S Why? Because x s ≤ f(null set) = 0

Submodular Function Minimization min T ⊆ S f(T) = f’(S) f’(U) = min T ⊆ U f(T) Optimization over EP f is easy = max{x(S) | x ∈ EP f’ } = max{x(S) | x ∈ EP f, x ≤ 0} Separation over EP f is easy Separation over EP f’ is easy Optimization over EP f’ is easy Hence Proved