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 255
... sequence of solutions visited by an interior point search should con- form to principle 6.2 ( a ) This sequence is not appropriate because it begins at boundary point ( 0,0 ) . ( b ) This could be the sequence of an interior point ...
... sequence of solutions visited by an interior point search should con- form to principle 6.2 ( a ) This sequence is not appropriate because it begins at boundary point ( 0,0 ) . ( b ) This could be the sequence of an interior point ...
Page 412
... sequence , any arcs must be passed in the forward direction , and no node may be visited more than once . Two of ... sequence ( 3 , 4 ) , ( 4 , 10 ) , ( 10 , 8 ) . The pattern 3-7- 6-5-8 of Figure 9.2 ( b ) is not a path because it ...
... sequence , any arcs must be passed in the forward direction , and no node may be visited more than once . Two of ... sequence ( 3 , 4 ) , ( 4 , 10 ) , ( 10 , 8 ) . The pattern 3-7- 6-5-8 of Figure 9.2 ( b ) is not a path because it ...
Page 484
... sequence 1-3-4-7 . Part ( b ) of Figure 10.4 shows some sequences that are not chains . The first is not connected , and the second repeats node 3 . Notice that chains need not observe direction on the arcs . This is how a chain differs ...
... sequence 1-3-4-7 . Part ( b ) of Figure 10.4 shows some sequences that are not chains . The first is not connected , and the second repeats node 3 . Notice that chains need not observe direction on the arcs . This is how a chain differs ...
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