Linked from

The 18 pages that link to Objective function, each with the reason it gives.

• Gradient descentRelated: The method repeatedly changes parameters to lower this function.

• Combinatorial optimizationRelated: It defines what makes one feasible discrete choice better than another.

• Constrained optimizationRelated: Its values determine which feasible point counts as best.

• Assignment problemRelated: Summing the selected pairing costs gives the objective to minimize.

• Approximation algorithmRelated: Approximation quality is measured against the objective’s optimal value.

• Feasible regionRelated: It ranks points in the feasible region but does not itself determine feasibility.

• Optimal controlRelated: It specifies which system outcomes count as better and what the controller optimizes.

• HeuristicRelated: A heuristic needs a criterion, explicit or implicit, for judging candidate results.

• Mathematical optimizationRelated: It quantifies what optimization seeks to maximize or minimize.

• Optimization problemRelated: Its values express what the optimization problem seeks to minimize or maximize.

• Shadow priceRelated: The objective determines the quantity whose improvement the shadow price measures.

• Vehicle routing problemRelated: Distance, time, or operating cost must be quantified before routes can be compared.

• Linear programming relaxationRelated: The same objective is usually optimized before and after relaxing constraints.

• Leonid KantorovichRelated: His models formalized goals such as maximizing output from limited resources.