Optimization - Lecture 5, Part 1 M. Pawan Kumar Slides available online

Slides:



Advertisements
Similar presentations
C&O 355 Mathematical Programming Fall 2010 Lecture 6 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA 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.
6.1 Vector Spaces-Basic Properties. Euclidean n-space Just like we have ordered pairs (n=2), and ordered triples (n=3), we also have ordered n-tuples.
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
C&O 355 Mathematical Programming Fall 2010 Lecture 20 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA A.
How should we define corner points? Under any reasonable definition, point x should be considered a corner point x What is a corner point?
Lecture 8 – Nonlinear Programming Models Topics General formulations Local vs. global solutions Solution characteristics Convexity and convex programming.
The Structure of Polyhedra Gabriel Indik March 2006 CAS 746 – Advanced Topics in Combinatorial Optimization.
Linear Programming Unit 2, Lesson 4 10/13.
CSCI 3160 Design and Analysis of Algorithms Tutorial 6 Fei Chen.
Linear Programming and Approximation
Totally Unimodular Matrices Lecture 11: Feb 23 Simplex Algorithm Elliposid Algorithm.
Introduction to Linear and Integer Programming Lecture 7: Feb 1.
Approximation Algorithm: Iterative Rounding Lecture 15: March 9.
Duality Lecture 10: Feb 9. Min-Max theorems In bipartite graph, Maximum matching = Minimum Vertex Cover In every graph, Maximum Flow = Minimum Cut Both.
Computer Algorithms Integer Programming ECE 665 Professor Maciej Ciesielski By DFG.
Last class Decision/Optimization 3-SAT  Independent-Set Independent-Set  3-SAT P, NP Cook’s Theorem NP-hard, NP-complete 3-SAT  Clique, Subset-Sum,
Linear Programming – Max Flow – Min Cut Orgad Keller.
Polyhedral Optimization Lecture 3 – Part 3 M. Pawan Kumar Slides available online
1 Linear Programming Supplements (Optional). 2 Standard Form LP (a.k.a. First Primal Form) Strictly ≤ All x j 's are non-negative.
Polyhedral Optimization Lecture 3 – Part 2
C&O 355 Mathematical Programming Fall 2010 Lecture 17 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA A.
C&O 355 Lecture 2 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A.
Polyhedral Optimization Lecture 1 – Part 2 M. Pawan Kumar Slides available online
C&O 355 Mathematical Programming Fall 2010 Lecture 19 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA A.
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.
Theory of Computing Lecture 13 MAS 714 Hartmut Klauck.
C&O 355 Mathematical Programming Fall 2010 Lecture 18 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A.
Polyhedral Optimization Lecture 4 – Part 2 M. Pawan Kumar Slides available online
Discrete Optimization Lecture 4 – Part 1 M. Pawan Kumar Slides available online
Polyhedral Optimization Lecture 5 – Part 2 M. Pawan Kumar Slides available online
I.4 Polyhedral Theory 1. Integer Programming  Objective of Study: want to know how to describe the convex hull of the solution set to the IP problem.
Optimization - Lecture 4, Part 1 M. Pawan Kumar Slides available online
Updated 21 April2008 Linear Programs with Totally Unimodular Matrices.
3.4: Linear Programming Objectives: Students will be able to… Use linear inequalities to optimize the value of some quantity To solve linear programming.
Approximation Algorithms Department of Mathematics and Computer Science Drexel University.
Discrete Optimization Lecture 2 – Part 2 M. Pawan Kumar Slides available online
C&O 355 Lecture 7 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A.
Fabio D’Andreagiovanni Lecture Notes on Total Unimodularity.
Unique Games Approximation Amit Weinstein Complexity Seminar, Fall 2006 Based on: “Near Optimal Algorithms for Unique Games" by M. Charikar, K. Makarychev,
Hon Wai Leong, NUS (CS6234, Spring 2009) Page 1 Copyright © 2009 by Leong Hon Wai CS6234: Lecture 4  Linear Programming  LP and Simplex Algorithm [PS82]-Ch2.
Linear Programming: Formulations, Geometry and Simplex Method Yi Zhang January 21 th, 2010.
Number of primal and dual bases of network flow and unimodular integer programs Hiroki NAKAYAMA 1, Takayuki ISHIZEKI 2, Hiroshi IMAI 1 The University of.
Discrete 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
C&O 355 Lecture 19 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.
Polyhedral Optimization Lecture 5 – Part 3 M. Pawan Kumar Slides available online
Linear Programming Chap 2. The Geometry of LP  In the text, polyhedron is defined as P = { x  R n : Ax  b }. So some of our earlier results should.
TU/e Algorithms (2IL15) – Lecture 12 1 Linear Programming.
Submodularity Reading Group Matroid Polytopes, Polymatroid M. Pawan Kumar
Polyhedral Optimization Lecture 2 – Part 2 M. Pawan Kumar Slides available online
CS4234 Optimiz(s)ation Algorithms L2 – Linear Programming.
Lap Chi Lau we will only use slides 4 to 19
Topics in Algorithms Lap Chi Lau.
Anticoloring Problems on Graphs
Proving that a Valid Inequality is Facet-defining
Polyhedron Here, we derive a representation of polyhedron and see the properties of the generators. We also see how to identify the generators. The results.
Polyhedron Here, we derive a representation of polyhedron and see the properties of the generators. We also see how to identify the generators. The results.
Linear Programming and Approximation
2. Generating All Valid Inequalities
Kramer’s (a.k.a Cramer’s) Rule
Lecture 20 Linear Program Duality
Flow Feasibility Problems
Proving that a Valid Inequality is Facet-defining
1.6 Linear Programming Pg. 30.
BASIC FEASIBLE SOLUTIONS
Chapter 2. Simplex method
Presentation transcript:

Optimization - Lecture 5, Part 1 M. Pawan Kumar Slides available online

Integer Linear Programming Duality Integer Polyhedron Totally Unimodular Matrices Outline

Linear Program s.t. A x ≤ b max x c T x

Integer Linear Program s.t. A x ≤ b max x c T x x is an integer vector Every element of x is an integer

Integer Linear Program s.t. A x ≤ b max x c T x x ∈ Z n Every element of x is an integer

Example 4x 1 – x 2 ≤ 8 max x x 1 + x 2 2x 1 + x 2 ≤ 10 5x 1 - 2x 2 ≥ -2 x 1 ≥ 0 x 2 ≥ 0 s.t. x ∈ Z n

Example x 1 ≥ 0 x 2 ≥ 0

Example 4x 1 – x 2 = 8 x 1 ≥ 0 x 2 ≥ 0

Example 4x 1 – x 2 ≤ 8 x 1 ≥ 0 x 2 ≥ 0

Example 4x 1 – x 2 ≤ 8 2x 1 + x 2 ≤ 10 x 1 ≥ 0 x 2 ≥ 0

Example 4x 1 – x 2 ≤ 8 2x 1 + x 2 ≤ 10 5x 1 - 2x 2 ≥ -2 x 1 ≥ 0 x 2 ≥ 0

Example 4x 1 – x 2 ≤ 8 2x 1 + x 2 ≤ 10 5x 1 - 2x 2 ≥ -2 x 1 ≥ 0 x 2 ≥ 0 x ∈ Z n max x c T x (2,6) (3,4) (2,0)(0,0) (0,1)

Example 4x 1 – x 2 ≤ 8 2x 1 + x 2 ≤ 10 5x 1 - 2x 2 ≥ -2 x 1 ≥ 0 x 2 ≥ 0 x ∈ Z n max x c T x (2,6) (3,4) (2,0)(0,0) (0,1) Why?True in general?

Example 4x 1 – x 2 ≤ 8 2x 1 + x 2 ≤ 10 5x 1 - 2x 2 ≥ -2 x 1 ≥ 0 x 2 ≥ 0 x ∈ Z n max x c T x (2,6) (3,4) (2,0)(0,0) (0,1)

Example 4x 1 – x 2 ≤ 8 2x 1 + x 2 ≤ 10 5x 1 - 2x 2 ≥ -2 x 1 ≥ 0.5 x 2 ≥ 0 x ∈ Z n max x c T x (2,6) (3,4) (2,0)(0,0) (0,1)

Example 4x 1 – x 2 ≤ 8 2x 1 + x 2 ≤ 10 5x 1 - 2x 2 ≥ -2 x 1 ≥ 0.5 x 2 ≥ 0 x ∈ Z n max x c T x (2,6) (3,4) (2,0) (0.5,0) (0.5,2.25)

ILP are generally NP-hard Integer Linear Program Many combinatorial optimization problems “Easy” ILP = Easy optimization problem

Integer Linear Programming Duality Integer Polyhedron Totally Unimodular Matrices Outline

s.t. A x ≤ b max x c T x Question x ∈ Z n s.t. A x ≤ b max x c T x ≤ ≥ =

s.t. A x ≤ b max x c T x Answer x ∈ Z n s.t. A x ≤ b max x c T x ≤

s.t. A x ≤ b max x c T x Question x ∈ Z n s.t. A x ≤ b max x c T x ≤ s.t. A y = c min y b T y ≤ ≥ =

s.t. A x ≤ b max x c T x Answer x ∈ Z n s.t. A x ≤ b max x c T x ≤ s.t. A y = c min y b T y =

s.t. A x ≤ b max x c T x Question x ∈ Z n s.t. A x ≤ b max x c T x ≤ s.t. A y = c min y b T y = s.t. A y = c min y b T y y ∈ Z m ≤ ≥ =

s.t. A x ≤ b max x c T x Answer x ∈ Z n s.t. A x ≤ b max x c T x ≤ s.t. A y = c min y b T y = s.t. A y = c min y b T y y ∈ Z m ≥

s.t. A x ≤ b max x c T x Duality Relationship x ∈ Z n s.t. A x ≤ b max x c T x ≤ s.t. A y = c min y b T y = s.t. A y = c min y b T y y ∈ Z m ≥ ≤

s.t. A x ≤ b max x c T x Duality Relationship x ∈ Z n s.t. A x ≤ b max x c T x ≤ s.t. A y = c min y b T y = s.t. A y = c min y b T y y ∈ Z m ≥ ≤ Strict inequality for some problems

Integer Linear Programming Duality Integer Polyhedron Totally Unimodular Matrices Outline

4x 1 – x 2 ≤ 8 2x 1 + x 2 ≤ 10 5x 1 - 2x 2 ≥ -2 x 1 ≥ 0 x 2 ≥ 0 (2,6) (3,4) (2,0)(0,0) (0,1) All the vertices of P are integer vectors P Integer Polyhedron Integer polytope is a bounded integer polyhedron

4x 1 – x 2 ≤ 8 2x 1 + x 2 ≤ 10 5x 1 - 2x 2 ≥ -2 x 1 ≥ 0 x 2 ≥ 0 (2,6) (3,4) (2,0)(0,0) (0,1) P Integer Polyhedron Integer polyhedron need not be bounded All the vertices of P are integer vectors

4x 1 – x 2 ≤ 8 5x 1 - 2x 2 ≥ -2 x 1 ≥ 0 x 2 ≥ 0 (2,6) (3,4) (2,0)(0,0) (0,1) P Integer Polyhedron Integer polyhedron need not be bounded All the vertices of P are integer vectors

4x 1 – x 2 ≤ 8 5x 1 - 2x 2 ≥ -2 x 1 ≥ 0 x 2 ≥ 0 Integer polyhedron need not be bounded P Integer Polyhedron (2,0)(0,0) (0,1) All the vertices of P are integer vectors

4x 1 – x 2 ≤ 8 5x 1 - 2x 2 ≥ -2 x 1 ≥ 0 x 2 ≥ 0 Are all polyhedra integer polyhedra? P Question (2,0)(0,0) (0,1) All the vertices of P are integer vectors NO

4x 1 – x 2 ≤ 8 5x 1 - 2x 2 ≥ -2 x 1 ≥ 0.5 x 2 ≥ 0 Are all polyhedra integer polyhedra? P Question (2,0)(0,0) (0,1) All the vertices of P are integer vectors NO

4x 1 – x 2 ≤ 8 5x 1 - 2x 2 ≥ -2 x 1 ≥ 0.5 x 2 ≥ 0 Are all polyhedra integer polyhedra? Question (2,0) All the vertices of P are integer vectors NO (0.5,0) (0.5,2.25) P

Integer polyhedra are very useful ILP over Integer polyhedron is easy Integer Polyhedron Drop the integrality constraints, solve LP But how can we identify integer polyhedra?

Integer Linear Programming Duality Integer Polyhedron Totally Unimodular Matrices Outline

Totally Unimodular Matrix A is a TUM For all square submatrix A’ of A det(A’) is 0, +1 or -1

Question Is this a TUM? All elements must be 0, +1 or -1 NO

Question Is this a TUM? Determinant of the matrix = -2 NO

Question 1111 Is this a TUM? YES.

Question 1 1 Is this a TUM? YES.

Property A is a TUM b is an integer vector, b ∈ Z n Polyhedron P = {x | Ax ≤ b} P is an integer polyhedronProof?

Proof Sketch Let v be a vertex of P. A’ is a full-rank square submatrix of A b’ is the corresponding subvector of b A’v = b’Why?

Proof Sketch Let v be a vertex of P. A’ is a full-rank square submatrix of A b’ is the corresponding subvector of b A’v = b’ v = adj(A’)b’/det(A’) v is integer Integer+1 or -1

Totally Unimodular Matrix How can we identify if A is TUM? O((m+n) 3 ) algorithm. K. Truemper, 1990 We don’t want to identify easy problem instances We want to identify easy problems

Totally Unimodular Matrix Some types of matrices are TUM Easy to prove that they are TUM Corresponding problems are provably “easy” We will see that min-cut is one such problem

Questions?