solver:solving_a_model_to_optimality

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solver:solving_a_model_to_optimality [2014/11/25 11:52] tlastusilta |
solver:solving_a_model_to_optimality [2017/09/02 19:32] (current) support |
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=== Local vs. global NLP/MINLP solver === | === Local vs. global NLP/MINLP solver === | ||

- | There are two types of non-linear solvers: local and global. A local solver is, in general, not able to prove that a found solution is globally optimal. However, they may still find a global optimal solution and many times they find a local optimal solution. A local optimal solution means that by doing small changes in the variable levels, it is not possible to find a solution with a better objective value. A global solver is able to find and prove that the final solution is globally optimal, i.e. there does not exist a solution that would result in a better objective value. The computational effort to solve a non-linear problem to global optimality is significantly higher and, therefore, the local solvers are typically used on larger problems, where a global solver is not expected to terminate in reasonable time. Furthermore, it is worth to note, that in some special cases, i.e. model formulations, a local solver can solve a model to global optimality, however, currently this is not reflected in the model status field of the solution report. Furthermore, note that a Quadratically Constrained Program (QCP) is a special type of Non-Linear Programming (NLP), that some solver handles in a specialized way. To see if a solver can solve a model to global optimality, see column Global and look for entries with *, on the following [[http://www.gams.com/modtype/index.htm|website]]. | + | There are two types of non-linear solvers: local and global. A local solver is, in general, not able to prove that a found solution is globally optimal. However, they may still find a global optimal solution and many times they find a local optimal solution. A local optimal solution means that by doing small changes in the variable levels, it is not possible to find a solution with a better objective value. A global solver is able to find and prove that the final solution is globally optimal, i.e. there does not exist a solution that would result in a better objective value. The computational effort to solve a non-linear problem to global optimality is significantly higher and, therefore, the local solvers are typically used on larger problems, where a global solver is not expected to terminate in reasonable time. Furthermore, it is worth to note, that in some special cases, i.e. model formulations, a local solver can solve a model to global optimality, however, currently this is not reflected in the model status field of the solution report. Furthermore, note that a Quadratically Constrained Program (QCP) is a special type of Non-Linear Programming (NLP), that some solver handles in a specialized way. To see if a solver can solve a model to global optimality, see column Global and look for entries with *, on the following [[https://www.gams.com/latest/docs/S_MAIN.html#SOLVERS_MODEL_TYPES|table]]. |

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solver/solving_a_model_to_optimality.txt ยท Last modified: 2017/09/02 19:32 by support