ECE 556 Linear Programming Ting-Yuan Wang Electrical and Computer Engineering University of Wisconsin-Madison March 4. 2002.

Slides:



Advertisements
Similar presentations
February 14, 2002 Putting Linear Programs into standard form
Advertisements

Lecture 3 Linear Programming: Tutorial Simplex Method
Operation Research Chapter 3 Simplex Method.
Linear Programming – Simplex Method
LECTURE 14 Minimization Two Phase method by Dr. Arshad zaheer
Dr. Sana’a Wafa Al-Sayegh
Copyright (c) 2003 Brooks/Cole, a division of Thomson Learning, Inc
Sections 4.1 and 4.2 The Simplex Method: Solving Maximization and Minimization Problems.
1. The Simplex Method for Problems in Standard Form 1.
Chapter 6 Linear Programming: The Simplex Method Section 3 The Dual Problem: Minimization with Problem Constraints of the Form ≥
Linear Inequalities and Linear Programming Chapter 5
The Simplex Method: Standard Maximization Problems
5.4 Simplex method: maximization with problem constraints of the form
The Simplex Algorithm An Algorithm for solving Linear Programming Problems.
Operation Research Chapter 3 Simplex Method.
Design and Analysis of Algorithms
Solving Linear Programs: The Simplex Method
Chapter 10: Iterative Improvement
Linear Programming (LP)
The Simplex Method.
5.6 Maximization and Minimization with Mixed Problem Constraints
Easy Optimization Problems, Relaxation, Local Processing for a small subset of variables.
MIT and James Orlin © Chapter 3. The simplex algorithm Putting Linear Programs into standard form Introduction to Simplex Algorithm.
LINEAR PROGRAMMING SIMPLEX METHOD.
Linear Programming - Standard Form
Chapter 6 Linear Programming: The Simplex Method
The Two-Phase Simplex Method LI Xiao-lei. Preview When a basic feasible solution is not readily available, the two-phase simplex method may be used as.
8. Linear Programming (Simplex Method) Objectives: 1.Simplex Method- Standard Maximum problem 2. (i) Greedy Rule (ii) Ratio Test (iii) Pivot Operation.
Simplex Algorithm.Big M Method
Topic III The Simplex Method Setting up the Method Tabular Form Chapter(s): 4.
Barnett/Ziegler/Byleen Finite Mathematics 11e1 Learning Objectives for Section 6.4 The student will be able to set up and solve linear programming problems.
1 1 Slide © 2000 South-Western College Publishing/ITP Slides Prepared by JOHN LOUCKS.
Kerimcan OzcanMNGT 379 Operations Research1 Linear Programming: The Simplex Method Chapter 5.
1 1 © 2003 Thomson  /South-Western Slide Slides Prepared by JOHN S. LOUCKS St. Edward’s University.
1 1 © 2003 Thomson  /South-Western Slide Slides Prepared by JOHN S. LOUCKS St. Edward’s University.
Simplex Method Adapting to Other Forms.  Until now, we have dealt with the standard form of the Simplex method  What if the model has a non-standard.
Mechanical Engineering Department 1 سورة النحل (78)
1 1 Slide © 2005 Thomson/South-Western Linear Programming: The Simplex Method n An Overview of the Simplex Method n Standard Form n Tableau Form n Setting.
Chapter 4 Linear Programming: The Simplex Method
Chapter 6 Linear Programming: The Simplex Method Section 4 Maximization and Minimization with Problem Constraints.
Barnett/Ziegler/Byleen Finite Mathematics 11e1 Learning Objectives for Section 6.3 The student will be able to formulate the dual problem. The student.
Gomory Cuts Updated 25 March Example ILP Example taken from “Operations Research: An Introduction” by Hamdy A. Taha (8 th Edition)“Operations Research:
1 Simplex Method (created by George Dantzig in late 1940s) A systematic way of searching for an optimal LP solution BMGT 434, Spring 2002 Instructor: Chien-Yu.
Linear Inequalities and Linear Programming Chapter 5 Dr.Hayk Melikyan/ Department of Mathematics and CS/ 5.5 Dual problem: minimization.
University of Colorado at Boulder Yicheng Wang, Phone: , Optimization Techniques for Civil and Environmental Engineering.
An-Najah N. University Faculty of Engineering and Information Technology Department of Management Information systems Operations Research and Applications.
Simplex Method Simplex: a linear-programming algorithm that can solve problems having more than two decision variables. The simplex technique involves.
Part 3. Linear Programming 3.2 Algorithm. General Formulation Convex function Convex region.
Copyright © 2006 Brooks/Cole, a division of Thomson Learning, Inc. Linear Programming: An Algebraic Approach 4 The Simplex Method with Standard Maximization.
Linear Programming 虞台文.
Simplex Method Review. Canonical Form A is m x n Theorem 7.5: If an LP has an optimal solution, then at least one such solution exists at a basic feasible.
Foundations-1 The Theory of the Simplex Method. Foundations-2 The Essence Simplex method is an algebraic procedure However, its underlying concepts are.
1 Simplex algorithm. 2 The Aim of Linear Programming A Linear Programming model seeks to maximize or minimize a linear function, subject to a set of linear.
Decision Support Systems INF421 & IS Simplex: a linear-programming algorithm that can solve problems having more than two decision variables.
GOOD MORNING CLASS! In Operation Research Class, WE MEET AGAIN WITH A TOPIC OF :
The Simplex Method. and Maximize Subject to From a geometric viewpoint : CPF solutions (Corner-Point Feasible) : Corner-point infeasible solutions 0.
Solving Linear Program by Simplex Method The Concept
Simplex Algorithm.Big M Method
Perturbation method, lexicographic method
10CS661 OPERATION RESEARCH Engineered for Tomorrow.
10CS661 OPERATION RESEARCH Engineered for Tomorrow.
Chapter 4 Linear Programming: The Simplex Method
The Simplex Method: Nonstandard Problems
Part 3. Linear Programming
Solving Linear Programming Problems: Asst. Prof. Dr. Nergiz Kasımbeyli
Well, just how many basic
LINEAR PROGRAMMING Example 1 Maximise I = x + 0.8y
Chapter 10: Iterative Improvement
Presentation transcript:

ECE 556 Linear Programming Ting-Yuan Wang Electrical and Computer Engineering University of Wisconsin-Madison March

Outline: Related Courses: CS525 Linear Programming CS726 Nonlinear Programming Theory and Applications CS730 Nonlinear Programming Algorithms Jordan Exchange Linear Programming (Simplex Method) Phase II Phase I

Jordan Exchange ( pivot operation) Consider a linear system of m equations:

Tableau Form

A Jordan exchange with pivot A rs is the process of interchanging the dependent variable y r and the independent variables x s. Process: 1.Solve the rth equation for x s in terms of x 1, x 2, …, x s-1, y r, x s+1,…,x n. Note A rs ≠ 0 2.Substitute for x s in the remaining equations 3.Write the new system in a new tableau form

Tableau Form

The Simplex Method A linear program (or a linear programming problem) is the problem of minimizing (or maximizing) a linear function subject to linear inequalities and linear equalities. The Simplex Method: First find a feasible vertex of the standard linear program. If none exists, the problem is infeasible. Starting at this feasible vertex, move to the adjacent vertex such that the objective function z strictly decreases. If no such adjacent vertex exists, then stop, the current vertex is a solution of the problem or the objective is unbounded.

LP standard form: Objective function Constraints Bounds LP canonical form:

Tableau form Basic variables (slack variables) Non-basic variables feasible: b  0

Example 1 Minimize: Subject to: X1X21 X3 =121 X4 =210 X5 =11 X6 =1-413 X7 =-4123 Z3-60 Tableau Form

Pivot Selection Rules 1.Pricing (Pivot Column s Selection ): The pivot column is any column s with a negative element in the bottom row. We choose the most negative element as pivot column, which gives the most steepest local descent in the objective function z. 2.Ratio Test (Pivot Row r Selection ): The pivot row is any row r such that

X1X51 X3 =3-23 X4 =31 X2 =11 X6 =-349 X7 =-324 Z-36-6 X6X51 X3 =212 X4 =310 X2 =-1/31/34 X1 =-1/34/33 X7 =1-515 Z12-15 Pivot (X 1,X 5 ) Pivot (X 1,X 6 )

Geometric Illustration Vertex 1: N{1,2} Vertex 3: N{5,6} Vertex 2: N{1,5} X1=0 X2=0 Feasible region X7=0 X5=0 X3=0 X4=0 X6=0 Z=-6 Z=0

Phase II Procedure 1.Formulate the problem into standard form. 2.Create an initial feasible tableau. 3.Determine the pivot column s by pricing rule. If none exists, then tableau is optimal. 4.Determine the pivot row r by ratio test. If none exists, then tableau is unbounded. 5.Exchange X B and X N using Jordan exchange on H rs. 6.Go to step (3).

Example 2 X1X5X7X4 X2= X6= X3= Z= X1X2X3X4 X5=-304 X6=-2003 X7=0-43 Z= X1X5X3X4 X2= X6= X7= Z= X1X5X7X4 X2= X6= X3= Z= Pivot (X 7,X 3 ) Pivot (X 6,X 1 ) Pivot (X 5,X 2 )

Example 3 X1X2 X3=12 X4= 6 Z=10 Pivot (X 4,X 2 ) X1X4 X3=-28 X2= 6 Z=21-6 X2=0 X4=0 X3=0 X1=0 Vertex 1: N{1,2} Vertex 2: N{1,4} Z=-6 Z=0

Example 4 X1X2 X3=21 X4=11 Z= 0 Pivot (X 3,X 2 ) X1X3 X2=21 X4=12 Z=-31 X2=0 X4=0 X3=0 X1=0 Vertex 2: N{1,3} Vertex 1: N{1,2} Unbounded !! Z=0

Phase I Procedure 1.If b>0, introduce the artificial variable x 0 ≥ 0 in all the constraints that are violated and set z 0 = x 0. 2.The first pivot is chosen in the x 0 column and the row with worst infeasibility. Then do Jordan exchange. 3.Apply the standard simplex pivot rules until an optimal tableau is obtained. If the optimal value is positive, the original problem has no feasible point. 4.Strike out the column corresponding to x 0 and the row corresponding to z 0. 5.Go to Phase II.

Infeasible ?? X0=0 X3=0 X1=0 X5=0 X2=0 X4=0 Infeasible vertex 1 Plane{(x1,x2,x0)|x0=0}

X0X0 X 3 =0,X 0 =2 X 1 =0,X 0 =2X 5 =0,X 0 =2 X 2 =0,X 0 =2 X 4 =0,X 0 =2 feasible vertex 2 (0,0,2) X1X2X01 X3=111 X4=211-2 X5=004 Z0=0010 X1X2X41 X3=011 X0=-212 X5=004 Z0=-212 X0X2X41 X3=0.5 0 X1= X5= Z0=1000 Plane{(x1,x2,x0)|x0=2}

Example 5 X1X2X3X41 X5=-304 X6=-2003 X7=0-43 X8=1120 X9=140 Z= Pivot (X 8,X 0 ) X1X2X3X4X01 X5=-3004 X6= X7=0-403 X8=11201 X9=1401 Z= Z0= Phase I Add column Add row

X1X2X3X4X81 X5=-3004 X6= X7=0-403 X0= X9= Z= Z0= X1X2X0X4X81 X5=-3004 X6= X7= X3= X9= Z= Z0= Pivot (X 0,X 3 ) X1X2X4X81 X5=-304 X6=-2003 X7=21-21 X3= X9=-3021 Z= Delete row X 0 & column Z 0 Pivot (X 3,X 2 ) Go to Phase II

X1X3X4X81 X5=26-31 X6=20 2 X7=1-2 2 X2=-2011 X9= Z=27-4 X1X3X4X51 X8= X6= X7= X2= X9= Z= X1X7X4X51 X8= X6= X3= X2= X9= 02 Z= X6X7X4X51 X8= X1= X3= X2= X9= Z= Pivot (X 5,X 8 ) Pivot (X 7,X 3 ) Pivot (X 6,X 1 )