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In general, a lexmaxmin optimization problem may have more than one optimal solution. If and are two optimal solutions, then their ordered value vector must be the same, that is, [] = [] for all [], [5]: Thm.2 that is, the smallest value is the same, the second-smallest value is the same, and so on.
Thm.2 Moreover, if the feasible domain is a convex set, and the objective functions are strictly concave, then the problem has at most one optimal solution, since if there were two different optimal solutions, their mean would be another feasible solution in which the objective functions attain a higher value - contradicting the optimality of ...
If is a bounded polyhedron (and thus a polytope) and is an optimal solution to the problem, then is either an extreme point (vertex) of , or lies on a face of optimal solutions. Proof [ edit ]
Infinite-dimensional optimization studies the case when the set of feasible solutions is a subset of an infinite-dimensional space, such as a space of functions. Heuristics and metaheuristics make few or no assumptions about the problem being optimized. Usually, heuristics do not guarantee that any optimal solution need be found.
Of particular use is the property that for any fixed set of ~ values, the optimal result to the Lagrangian relaxation problem will be no smaller than the optimal result to the original problem. To see this, let x ^ {\displaystyle {\hat {x}}} be the optimal solution to the original problem, and let x ¯ {\displaystyle {\bar {x}}} be the optimal ...
Optimal substructure means that the solution to a given optimization problem can be obtained by the combination of optimal solutions to its sub-problems. Such optimal substructures are usually described by means of recursion. For example, given a graph G=(V,E), the shortest path p from a vertex u to a vertex v exhibits optimal substructure ...
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The goal is then to find for some instance x an optimal solution, that is, a feasible solution y with (,) = {(, ′): ′ ()}. For each combinatorial optimization problem, there is a corresponding decision problem that asks whether there is a feasible solution for some particular measure m 0 .