User Tools

Site Tools


solver:solving_a_model_to_optimality

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
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
Line 27: Line 27:
 === 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]]. 
  
  
  
IMPRESSUM / LEGAL NOTICEPRIVACY POLICY solver/solving_a_model_to_optimality.txt ยท Last modified: 2017/09/02 19:32 by support