MLP solver is a heuristic method. A local optimum is better than all nearby points. An optimization problem can only have one local optimal solution. A global optimum is the best point in the entire feasible region. An optimization problem can only have one global optimal solution. For linear optimization problems any local optimal solution is a global optimal solution. For some nonlinear programming problems, Solver can get stuck at a local optimum and never find the global optimum. Forsomelinearprogrammingproblems,Solvercangetstuckatalocaloptimumandnever find the global optimum. Solver is guaranteed to find the global optimum for convex nonlinear optimization problems. A heuristic is a relatively simple solution method that often provides good but not necessarily optimal solutions.

MLP solver is a heuristic method. A local optimum is better than all nearby points. An optimization problem can only have one local optimal solution. A global optimum is the best point in the entire feasible region. An optimization problem can only have one global optimal solution. For linear optimization problems any local optimal solution is a global optimal solution. For some nonlinear programming problems, Solver can get stuck at a local optimum and never find the global optimum. Forsomelinearprogrammingproblems,Solvercangetstuckatalocaloptimumandnever find the global optimum. Solver is guaranteed to find the global optimum for convex nonlinear optimization problems. A heuristic is a relatively simple solution method that often provides good but not necessarily optimal solutions.

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter8: Evolutionary Solver: An Alternative Optimization Procedure

Section8.3: Introduction To Evolutionary Solver

Problem 2P

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Which of the following is true? a)The maximin criterion is an approach in Optimization under uncertainty which finds a solution that has the best possible payoff. b)The maximin criterion is an approach in Optimization under uncertainty which finds a solution with the best worst possible payoff. c)A risk profile represents the probability distribution of uncertain inputs. d)Decision tree is a method to solve any optimization problem when the outcomes are subject to uncertainty.
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