C&O 355 Lecture 2 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A.

Slides:



Advertisements
Similar presentations
C&O 355 Lecture 15 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA A A A A A A A A.
Advertisements

Min-Max Relations, Hall’s Theorem, and Matching-Algorithms Graphs & Algorithms Lecture 5 TexPoint fonts used in EMF. Read the TexPoint manual before you.
C&O 355 Lecture 6 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A.
C&O 355 Lecture 8 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A.
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.
Incremental Linear Programming Linear programming involves finding a solution to the constraints, one that maximizes the given linear function of variables.
C&O 355 Lecture 5 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A.
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.
Lecture #3; Based on slides by Yinyu Ye
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.
Introduction to Algorithms
Dragan Jovicic Harvinder Singh
C&O 355 Lecture 4 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A.
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.
C&O 355 Mathematical Programming Fall 2010 Lecture 21 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 15 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA A.
C&O 355 Lecture 20 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.
C&O 355 Lecture 12 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.
How should we define corner points? Under any reasonable definition, point x should be considered a corner point x What is a corner point?
Basic Feasible Solutions: Recap MS&E 211. WILL FOLLOW A CELEBRATED INTELLECTUAL TEACHING TRADITION.
A Randomized Polynomial-Time Simplex Algorithm for Linear Programming Daniel A. Spielman, Yale Joint work with Jonathan Kelner, M.I.T.
CS38 Introduction to Algorithms Lecture 15 May 20, CS38 Lecture 15.
CSCI 3160 Design and Analysis of Algorithms Tutorial 6 Fei Chen.
Totally Unimodular Matrices Lecture 11: Feb 23 Simplex Algorithm Elliposid Algorithm.
Design and Analysis of Algorithms
1 Introduction to Linear and Integer Programming Lecture 9: Feb 14.
Introduction to Linear and Integer Programming Lecture 7: Feb 1.
Matching Polytope, Stable Matching Polytope Lecture 8: Feb 2 x1 x2 x3 x1 x2 x3.
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 Mathematical Programming Fall 2010 Lecture 1 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA A A.
C&O 355 Mathematical Programming Fall 2010 Lecture 2 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA A A.
Computational Geometry Piyush Kumar (Lecture 5: Linear Programming) Welcome to CIS5930.
Linear Programming Piyush Kumar. Graphing 2-Dimensional LPs Example 1: x y Feasible Region x  0y  0 x + 2 y  2 y  4 x  3 Subject.
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.
Approximating Minimum Bounded Degree Spanning Tree (MBDST) Mohit Singh and Lap Chi Lau “Approximating Minimum Bounded DegreeApproximating Minimum Bounded.
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.
Pareto Linear Programming The Problem: P-opt Cx s.t Ax ≤ b x ≥ 0 where C is a kxn matrix so that Cx = (c (1) x, c (2) x,..., c (k) x) where c.
Linear Programming Data Structures and Algorithms A.G. Malamos References: Algorithms, 2006, S. Dasgupta, C. H. Papadimitriou, and U. V. Vazirani Introduction.
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.
TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA A A A A A A A A Image:
Linear Programming Problem. Definition A linear programming problem is the problem of optimizing (maximizing or minimizing) a linear function (a function.
The Simplex Algorithm 虞台文 大同大學資工所 智慧型多媒體研究室. Content Basic Feasible Solutions The Geometry of Linear Programs Moving From Bfs to Bfs Organization of a.
§1.4 Algorithms and complexity For a given (optimization) problem, Questions: 1)how hard is the problem. 2)does there exist an efficient solution algorithm?
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.
C&O 355 Mathematical Programming Fall 2010 Lecture 5 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA A A.
C&O 355 Lecture 7 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A.
CPSC 536N Sparse Approximations Winter 2013 Lecture 1 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAAAAAAAAA.
C&O 355 Lecture 24 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA A A A A A A A A.
Linear Programming: Formulations, Geometry and Simplex Method Yi Zhang January 21 th, 2010.
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.
TU/e Algorithms (2IL15) – Lecture 12 1 Linear Programming.
Common Intersection of Half-Planes in R 2 2 PROBLEM (Common Intersection of half- planes in R 2 ) Given n half-planes H 1, H 2,..., H n in R 2 compute.
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.
Linear Programming Piyush Kumar Welcome to CIS5930.
Approximation Algorithms based on linear programming.
TU/e Algorithms (2IL15) – Lecture 12 1 Linear Programming.
1 Chapter 4 Geometry of Linear Programming  There are strong relationships between the geometrical and algebraic features of LP problems  Convenient.
Discrete Optimization
Lap Chi Lau we will only use slides 4 to 19
ADVANCED COMPUTATIONAL MODELS AND ALGORITHMS
Topics in Algorithms Lap Chi Lau.
Analysis of Algorithms
Lecture 20 Linear Program Duality
C&O 355 Lecture 3 N. Harvey Review of Lecture 2:
Part 3. Linear Programming
Chapter 2. Simplex method
Graphical solution A Graphical Solution Procedure (LPs with 2 decision variables can be solved/viewed this way.) 1. Plot each constraint as an equation.
Chapter 2. Simplex method
Presentation transcript:

C&O 355 Lecture 2 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

Outline LP definition & some equivalent forms Example in 2D – Possible outcomes Examples – Linear regression, bipartite matching, indep set Feasible Region, Convex Sets Corner solutions & certificates Local-Search Algorithm

Linear Program General definition – Parameters: c, a 1,…,a m 2 R n, b 1,…,b m 2 R – Variables: x 2 R n Terminology – Feasible point: any x satisfying constraints – Optimal point: any feasible x that minimizes obj. func – Optimal value: value of obj. func for any optimal point Objective function Constraints

Matrix form Parameters: c 2 R n, A 2 R m £ n, b 2 R m Variables: x 2 R n Linear Program General definition – Parameters: c, a 1,…,a m 2 R n, b 1,…,b m 2 R – Variables: x 2 R n

Simple LP Manipulations “max” instead of “min” max c T x ´ min –c T x “ ¸ ” instead of “ · ” a T x ¸ b, -a T x · -b “=” instead of “ · ” a T x=b, a T x · b and a T x ¸ b

x1x1 x2x2 x 2 - x 1 · 1 x 1 + 6x 2 · 15 4x 1 - x 2 · 10 (1,1) (0,0) Feasible region Constraint Optimal point 2D Example (Textbook, Ch 1) x1¸0x1¸0 x2¸0x2¸0 Unique optimal solution exists (3,2)

x1x1 x2x2 x 2 - x 1 · 1 x 1 + 6x 2 · 15 4x 1 - x 2 · 10 (1/6,1) (0,0) Feasible region Constraint Optimal points 2D Example x1¸0x1¸0 x2¸0x2¸0 Optimal solutions exist: Infinitely many!

x1x1 x2x2 x 2 - x 1 ¸ 1 x 1 + 6x 2 · 15 4x 1 - x 2 ¸ 10 (1,1) (0,0) No feasible region Constraint 2D Example (Textbook, Ch 1) x1¸0x1¸0 x2¸0x2¸0 Infeasible No feasible solutions

x1x1 x2x2 x 2 - x 1 · 1 (1,1) (0,0) Feasible region Constraint 2D Example (Textbook, Ch 1) x1¸0x1¸0 x2¸0x2¸0 Unbounded Feasible solutions, but no optimal solution (Optimal value = 1 )

“Fundamental Theorem” of LP Theorem: For any LP, the outcome is either: – Optimal solution (unique or infinitely many) – Infeasible – Unbounded (optimal value is 1 or - 1 ) Proof: Later in the course!

Example: Linear regression Given data (x 1, y 1 ), …, (x n, y n ) in R 2 Find a line y = ax + b that fits the points Usual setupOur setup Not differentiable! Easy: differentiate, set to zero Absolute value trick: ´ Our setup can be written as an LP

Example: Bipartite Matching Given bipartite graph G=(V, E) Find a maximum size matching – A set M µ E s.t. every vertex has at most one incident edge in M But we don’t know how to solve IPs. Try an LP instead. (LP) Theorem: (IP) and (LP) have the same solution! Proof: Later in the course! Corollary: Bipartite matching can be solved efficiently (it’s in P). Write an integer program (IP)

Example: Independent Set Given graph G=(V, E) Find a maximum size independent set – A set U µ V s.t. {u,v}  E for every distinct u,v 2 U But we don’t know how to solve IPs. Try an LP instead. (LP) Unfortunately (IP) and (LP) are extremely different. Fact: There are graphs for which OPT LP / OPT IP ¸ |V|/2. Write an integer program (IP)

Feasible Region For any a 2 R n, b 2 R, define So feasible region of is Intersection of finitely many halfspaces is polyhedron A bounded polyhedron is a polytope, i.e., P µ { x : -M · x i · M 8 i } for some M Hyperplane Halfspaces

Convex Sets C µ R n is convex if for every x,y 2 C, C contains line segment between x and y. i.e., 8 ® 2 [0,1], we have ® x + (1- ® )y 2 C. Claim 1: Any halfspace is convex. Claim 2: The intersection of any number of convex sets is convex. Corollary: Any polyhedron is convex. Not convex x y Convex x y Such a point is called a convex combination of x and y

Where are optimal solutions? x1x1 x2x2 x 2 - x 1 · 1 x 1 + 6x 2 · 15 4x 1 - x 2 · 10

Where are optimal solutions? x1x1 x2x2 x 2 - x 1 · 1 x 1 + 6x 2 · 15 4x 1 - x 2 · 10

Where are optimal solutions? x1x1 x2x2 x 2 - x 1 · 1 x 1 + 6x 2 · 15 4x 1 - x 2 · 10 Lemma: For any objective function, a “corner point” is an optimal solution. (Assuming an optimal solution exists and some corner point exists). Proof: Later in the course!

Proving optimality x1x1 x2x2 -x 1 +x 2 · 1 x 1 + 6x 2 · 15 4x 1 - x 2 · 10 Question: What is optimal point in direction c = (-7,14)? Solution: Optimal point is x=(9/7,16/7), optimal value is 23. How can I be sure? – Every feasible point satisfies x 1 +6x 2 · 15 – Every feasible point satisfies -x 1 +x 2 · 1 ) -8x 1 +8x 2 · 8 – Every feasible point satisfies their sum: -7x 1 +14x 2 · 23 (9/7,16/7) This is the objective function!

Proving optimality Question: What is optimal point in direction c = (-7,14)? Solution: Optimal point is x=(9/7,16/7), optimal value is 23. How can I be sure? – Every feasible point satisfies x 1 +6x 2 · 15 – Every feasible point satisfies -x 1 +x 2 · 1 ) -8x 1 +8x 2 · 8 – Every feasible point satisfies their sum: -7x 1 +14x 2 · 23 Certificates To convince you that optimal value is ¸ k, I can find x such that c T x ¸ k. To convince you that optimal value is · k, I can find a linear combination of the constraints which proves that c T x · k. Theorem: Such certificates always exists. Proof: Later in the course! This is the objective function!

Local-Search Algorithm (The “Simplex Method”) “The obvious idea of moving along edges from one vertex of a convex polygon to the next” [Dantzig, 1963] Algorithm Let x be any corner point For each neighbor y of x If c T y>c T x then set x=y Halt In practice, very efficient Its analysis proves all the theorems mentioned earlier Every known variant of this algorithm takes exponential time Open problem: does some variant run in polynomial time?

Pitfalls and missing details Algorithm Let x be any corner point For each neighbor y of x If c T y>c T x then set x=y Halt 1.What is a corner point? 2.What if there are no corner points? 3.What are the “neighboring” corner points? 4.What if there are no neighboring corner points? 5.How can I find a starting corner point? 6.Does the algorithm terminate? 7.Does it produce the right answer?