Optimization in Operations ResearchProblem solving with mathematical models - Deterministic optimization models in operations research - Improving search - Linear programming models - Simplex search for linear programming - Interior point methods for linear programming - Duality and sensitivity in linear programming - Multiobjetive optimization and goal programming - Shortest paths and discrete dynamic programming - Network flows - Discrete optimization models - Discrete optimization methods - Unconstrained nonlinear programming - Constrained nonlinear programming. |
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Page 32
Ronald L. Rardin. 2.11 Objective functions are normally plotted in the same coordinate system as the feasible set of ... function value . To introduce contours , we begin with any convenient point visible in the plot of the feasible space ...
Ronald L. Rardin. 2.11 Objective functions are normally plotted in the same coordinate system as the feasible set of ... function value . To introduce contours , we begin with any convenient point visible in the plot of the feasible space ...
Page 112
... objectives . Unimodal Objective Functions and Unconstrained Local Optima Unconstrained local optima are solutions for which no point in some surrounding neighborhood has a better objective function value . That is , they are local ...
... objectives . Unimodal Objective Functions and Unconstrained Local Optima Unconstrained local optima are solutions for which no point in some surrounding neighborhood has a better objective function value . That is , they are local ...
Page 314
... objective function coefficients must await the duality development of the next few sections . For now , return to your struggling hero role , this time by manipulating the unit cost or benefit of some activity . Changing your objective ...
... objective function coefficients must await the duality development of the next few sections . For now , return to your struggling hero role , this time by manipulating the unit cost or benefit of some activity . Changing your objective ...
Contents
CHAPTER | 1 |
IN OPERATIONS RESEARCH | 23 |
IMPROVING SEARCH | 77 |
Copyright | |
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Optimization in Operations Research: Pearson New International Edition Ronald L. Rardin No preview available - 2013 |
Common terms and phrases
active Algorithm Analysis arcs assignment basic solution basic variables branch and bound CFPL choose class optimization software coefficient components compute convex corresponding cycle direction d₁ decision variables demand digraph direction Ax discrete example feasible direction feasible set feasible solution Figure Formulate global goal program gradient graph improving feasible improving search incumbent solution inequality infeasible integer integer linear program iteration Lagrange multipliers linear program local optimum LP relaxation main constraints matrix max s.t. maximize maximum minimize move direction multiobjective negative dicycle network flow node nonbasic nonlinear program nonnegative objective function objective function value objective value optimal path optimal solution optimal value optimization model partial solution posynomial primal principle problem produce quadratic SAMPLE EXERCISE schedule Section sequence shortest path shortest path problems shows simplex algorithm simplex direction slack solve standard form Step Table unconstrained v₁ vector w₁ x₁ y₁