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UMass Lowell Computer Science 91.503 Analysis of Algorithms Prof. Karen Daniels Fall, 2002 Lecture 8 Tuesday, 11/19/02 Linear Programming

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Overview ä Motivation & Basics ä Standard & Slack Forms ä Formulating Problems as Linear Programs ä Simplex Algorithm ä Duality ä Initial Basic Feasible Solution ä Literature Case Study

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Motivation & Basics

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Motivation: A Political Problem source: 91.503 textbook Cormen et al. Goal: Win election by winning majority of votes in each region. Subgoal: Win majority of votes in each region while minimizing advertising cost. 100,000 voters 200,000 voters 50,000 voters Thousands of voters who could be won with $1,000 of ads

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Motivation: A Political Problem (continued) Thousands of voters representing majority. urban suburban rural source: 91.503 textbook Cormen et al.

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General Linear Programs real numbers variables Linear function Linear inequalities Linear constraints source: 91.503 textbook Cormen et al.

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Overview of Linear Programming Convex feasible region Objective function Objective value source: 91.503 textbook Cormen et al.

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Standard & Slack Forms

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Standard Form objective function constraints source: 91.503 textbook Cormen et al.

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Standard Form (compact) mxn matrix m-dimensional vector n-dimensional vectors source: 91.503 textbook Cormen et al. Can specify linear program in standard form by (A,b,c).

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Converting to Standard Form source: 91.503 textbook Cormen et al.

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Converting to Standard Form (continued) source: 91.503 textbook Cormen et al. Negate coefficients Transforming minimization to maximization

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Converting to Standard Form (continued) source: 91.503 textbook Cormen et al. If x j has no non-negativity constraint, replace each occurrence of x j with x j ’ – x j ”. Giving each variable a non-negativity constraint New non-negativity constraints

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Converting to Standard Form (continued) source: 91.503 textbook Cormen et al. Transforming equality constraints to inequality constraints

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Converting to Standard Form (continued) source: 91.503 textbook Cormen et al. Changing sense of an inequality constraint Rationale:

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Converting Linear Programs into Slack Form source: 91.503 textbook Cormen et al. for algorithmic ease, transform all constraints except non-negativity ones into equalities for inequality constraint: define slack slack variable instead of s basic variables non-basic variables

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Converting Linear Programs into Slack Form (continued) source: 91.503 textbook Cormen et al. objective function

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Converting Linear Programs into Slack Form (continued) source: 91.503 textbook Cormen et al. Compact Form: (N, B, A, b, c, v) set of indices of non-basic variables set of indices of basic variables Slack Form Example Compact Form negative of slack form coefficients

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Formulating Problems as Linear Programs

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Shortest Paths source: 91.503 textbook Cormen et al. Single-pair shortest path: minimize “distance” from source s to sink t. Can we replace maximize with minimize here? Why or why not?

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Maximum Flow source: 91.503 textbook Cormen et al.

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Minimum Flow source: 91.503 textbook Cormen et al.

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Multicommodity Flow source: 91.503 textbook Cormen et al. should be s i

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Simplex Method

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Solving a Linear Program source: 91.503 textbook Cormen et al. ä Simplex algorithm ä Geometric interpretation ä Visit vertices on the boundary of the simplex representing the convex feasible region ä Transforms set of inequalities using process similar to Gaussian elimination ä Run-time ä not polynomial in worst-case ä often very fast in practice ä Ellipsoid method ä Run-time ä polynomial ä slow in practice ä Interior-Point methods ä Run-time ä polynomial ä for large inputs, performance can be competitive with simplex method ä Moves through interior of feasible region

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Simplex Algorithm: Example Basic Solution source: 91.503 textbook Cormen et al. Standard Form Slack Form Basic Solution: set each nonbasic variable to 0. Basic Solution:

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Simplex Algorithm: Example Reformulating the LP Model source: 91.503 textbook Cormen et al. Main Idea: In each iteration, reformulate the LP model so basic solution has larger objective value Select a nonbasic variable whose objective coefficient is positive: x 1 Increase its value as much as possible. Identify tightest constraint on increase. For basic variable x 6 of that constraint, swap role with x 1. Rewrite other equations with x 6 on RHS. PIVOT leaving variable entering variable new objective value

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Simplex Algorithm: Example Reformulating the LP Model source: 91.503 textbook Cormen et al. Next Iteration: select x 3 as entering variable. PIVOT leaving variable entering variable New Basic Solution: new objective value

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Simplex Algorithm: Example Reformulating the LP Model source: 91.503 textbook Cormen et al. Next Iteration: select x 2 as entering variable. PIVOT leaving variable entering variable New Basic Solution: new objective value

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Simplex Algorithm: Issues to be Addressed… source: 91.503 textbook Cormen et al.

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Simplex Algorithm: Pivoting source: 91.503 textbook Cormen et al. leaving variable entering variable Rewrite the equation that has x l on LHS to have x e on LHS Update remaining equations by substituting RHS of new equation for each occurrence of x e. Do the same for objective function. Update sets of nonbasic, basic variables.

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Simplex Algorithm: Pivoting (continued) source: 91.503 textbook Cormen et al.

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Simplex Algorithm: Pseudocode source: 91.503 textbook Cormen et al. to be defined later (detects infeasibility) Feasibility initial basic solution optimal solution

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Simplex Algorithm: Correctness Proof Strategy source: 91.503 textbook Cormen et al. ä If SIMPLEX has initial feasible solution and eventually terminates, then it either ä returns a feasible solution or ä determines that the LP is unbounded. ä SIMPLEX terminates. ä SIMPLEX’s solution is optimal.

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Simplex Algorithm: Correctness Proof Approach, Part I source: 91.503 textbook Cormen et al. ä If SIMPLEX has initial feasible solution and eventually terminates, then it either ä returns a feasible solution or ä determines that the LP is unbounded.

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Simplex Algorithm: Correctness Proof Approach, Part II source: 91.503 textbook Cormen et al.

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Duality Simplex Algorithm: Correctness Proof Approach, Part III

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Linear Programming Duality source: 91.503 textbook Cormen et al. max becomes min RHS coefficients swap places with objective function coefficients sense changes x variables go away y variables appear

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Duality Example source: 91.503 textbook Cormen et al.

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Weak Linear Programming Duality source: 91.503 textbook Cormen et al. Any feasible solution to primal LP has value no greater than that of any feasible solution to the dual LP.

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Weak Linear Programming Duality (continued) source: 91.503 textbook Cormen et al.

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Finding a Dual Solution source: 91.503 textbook Cormen et al. Finding a dual solution whose value is equal to that of an optimal primal solution…

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Optimality source: 91.503 textbook Cormen et al.

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Initial Basic Feasible Solution

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Finding an Initial Solution source: 91.503 textbook Cormen et al. An LP model whose initial basic solution is not feasible

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Finding an Initial Solution (continued) source: 91.503 textbook Cormen et al. Auxiliary LP model L aux :

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Finding an Initial Solution (continued) source: 91.503 textbook Cormen et al.

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Finding an Initial Solution (continued) source: 91.503 textbook Cormen et al. Original LP model L aux L aux in slack form

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Finding an Initial Solution (continued) source: 91.503 textbook Cormen et al. PIVOT

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Finding an Initial Solution (continued) source: 91.503 textbook Cormen et al.

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Fundamental Theorem of Linear Programming source: 91.503 textbook Cormen et al.

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Literature Case Study

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UMass Lowell Computer Science 91.503 Analysis of Algorithms Prof. Karen Daniels Spring, 2010 Lecture 9 Tuesday, 4/20/10 Linear Programming 1.

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