Introduction to Operations ResearchCD-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 573
... following nonlinear programming problem . Maximize subject to and x2 + x2 ≤ 4 z = 2x1 + x2 , X2 ≥ 0 . Use dynamic programming to solve this problem . 11.3-18 . Consider the following nonlinear programming problem . z CHAPTER 11 PROBLEMS ...
... following nonlinear programming problem . Maximize subject to and x2 + x2 ≤ 4 z = 2x1 + x2 , X2 ≥ 0 . Use dynamic programming to solve this problem . 11.3-18 . Consider the following nonlinear programming problem . z CHAPTER 11 PROBLEMS ...
Page 574
... programming problem . z = x + 2x2 Minimize subject to x2 + x2 ≥ 2 . ( There are no nonnegativity constraints . ) Use dynamic program- ming to solve this problem . 11.3-19 . Consider the following nonlinear programming problem ...
... programming problem . z = x + 2x2 Minimize subject to x2 + x2 ≥ 2 . ( There are no nonnegativity constraints . ) Use dynamic program- ming to solve this problem . 11.3-19 . Consider the following nonlinear programming problem ...
Page 712
... problem is a convex programming problem . ( b ) Use the KKT conditions to derive an optimal solution . ( c ) Use intuitive reasoning to demonstrate that the solution obtained in part ( b ) is indeed optimal . [ Hint : Note that ln ( 1 + ...
... problem is a convex programming problem . ( b ) Use the KKT conditions to derive an optimal solution . ( c ) Use intuitive reasoning to demonstrate that the solution obtained in part ( b ) is indeed optimal . [ Hint : Note that ln ( 1 + ...
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Introduction to Operations Research Frederick S. Hillier,Gerald J. Lieberman No preview available - 2001 |
Common terms and phrases
activity algebraic algorithm allowable range artificial variables b₂ basic solution c₁ c₂ changes coefficients column Consider the following cost Courseware CPLEX decision variables described dual problem dynamic programming entering basic variable estimates example feasible region feasible solutions final simplex tableau final tableau flow following problem formulation functional constraints Gaussian elimination given graphical identify increase initial BF solution integer iteration leaving basic variable linear programming model linear programming problem LINGO LP relaxation lution Maximize Z maximum flow problem Minimize needed node nonbasic variables nonnegativity constraints objective function obtained optimal solution optimality test parameters path plant presented in Sec primal problem Prob procedure range to stay resource right-hand sides sensitivity analysis shadow prices shown simplex method slack variables solve the model Solver spreadsheet step subproblem surplus variables Table tion unit profit values weeks Wyndor Glass x₁ zero