## Introduction to Operations Research, Volume 1CD-ROM contains: Student version of MPL Modeling System and its solver CPLEX -- MPL tutorial -- Examples from the text modeled in MPL -- Examples from the text modeled in LINGO/LINDO -- Tutorial software -- Excel add-ins: TreePlan, SensIt, RiskSim, and Premium Solver -- Excel spreadsheet formulations and templates. |

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Page 164

Now let us look at the key idea behind Karmarkar's

Now let us look at the key idea behind Karmarkar's

**algorithm**and its subsequent variants that use the interior - point approach . The Key Solution Concept Although radically different from the simplex method , Karmarkar's**algorithm**does ...Page 616

easier to fathom with this new fathoming test ( in either form ) , so the

easier to fathom with this new fathoming test ( in either form ) , so the

**algorithm**should run much faster . For a large problem , this acceleration may make the difference between finishing with a solution guaranteed to be close to ...Page 1082

Use the policy improvement

Use the policy improvement

**algorithm**to find an optimal policy for Prob . 21.2-6 . D.1 21.4-7 . Use the policy improvement**algorithm**to find an optimal policy for Prob . 21.2-7 . a 21.3-4 . Reconsider Prob . 21.2-4 .### What people are saying - Write a review

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### Other editions - View all

Introduction to Operations Research Frederick S. Hillier,Gerald J. Lieberman No preview available - 2001 |

### Common terms and phrases

activity algebraic algorithm allocation allowable range artificial variables assignment problem augmenting path basic solution Big M method changes coefficients column Consider the following constraint boundary corresponding CPLEX decision variables dual problem dynamic programming entering basic variable example feasible region feasible solutions final simplex tableau final tableau following problem formulation functional constraints Gaussian elimination given goal goal programming graphically identify increase initial BF solution integer interior-point iteration leaving basic variable linear programming model linear programming problem LP relaxation lution Maximize Maximize Z maximum flow problem Minimize needed node nonbasic variables objective function obtained optimal solution optimality test path Plant presented in Sec primal problem Prob procedure range to stay resource right-hand sides sensitivity analysis shadow prices slack variables solve this model Solver spreadsheet step subproblem surplus variables tion transportation problem transportation simplex method weeks Wyndor Glass x₁ zero