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Full-Text Articles in Physical Sciences and Mathematics
Tuning Optimization Software Parameters For Mixed Integer Programming Problems, Toni P. Sorrell
Tuning Optimization Software Parameters For Mixed Integer Programming Problems, Toni P. Sorrell
Theses and Dissertations
The tuning of optimization software is of key interest to researchers solving mixed integer programming (MIP) problems. The efficiency of the optimization software can be greatly impacted by the solver’s parameter settings and the structure of the MIP. A designed experiment approach is used to fit a statistical model that would suggest settings of the parameters that provided the largest reduction in the primal integral metric. Tuning exemplars of six and 59 factors (parameters) of optimization software, experimentation takes place on three classes of MIPs: survivable fixed telecommunication network design, a formulation of the support vector machine with the ramp …
Parameter Tuning For Optimization Software, Radhashilpa Koripalli
Parameter Tuning For Optimization Software, Radhashilpa Koripalli
Theses and Dissertations
Mixed integer programming (MIP) problems are highly parameterized, and finding parameter settings that achieve high performance for specific types of MIP instances is challenging. This paper presents a method to find the information about how CPLEX solver parameter settings perform for the different classes of mixed integer linear programs by using designed experiments and statistical models. Fitting a model through design of experiments helps in finding the optimal region across all combinations of parameter settings. The study involves recognizing the best parameter settings that results in the best performance for a specific class of instances. Choosing good setting has a …
Automated Selected Of Mixed Integer Program Solver Parameters, Charles Stewart
Automated Selected Of Mixed Integer Program Solver Parameters, Charles Stewart
Theses and Dissertations
This paper presents a method that uses designed experiments and statistical models to extract information about how solver parameter settings perform for classes of mixed integer programs. The use of experimental design facilitates fitting a model that describes the response surface across all combinations of parameter settings, even those not explicitly tested, allowing identification of both desirable and poor settings. Identifying parameter settings that give the best expected performance for a specific class of instances and a specific solver can be used to more efficiently solve a large set of similar instances, or to ensure solvers are being compared at …