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Welcome to the new SigOpt docs! If you're looking for the classic SigOpt documentation then you can find that here. Otherwise, happy optimizing!

Multi Solution

For Experiments where a modeler is interested in exploring multiple solutions, SigOpt will attempt to find additional solutions that are sufficiently diverse, and return multiple sets of parameters.

Example with Multiple Solutions

The contour plots below depict a function where two solutions are sufficiently far apart, and both are close to the absolute maximum of the function.

Defining an Experiment with Multiple Solutions in SigOpt

A SigOpt Experiment with multiple solutions can be conducted to return multiple sets of parameters that are sufficiently diverse. In the experiment_create call, add the key num_solutions and value as the number of solutions desired. A budget is required for experiments with multiple solutions, and cannot be updated for a given experiment.

Best Runs for Multiple Solutions

Calling Best Runs will return a list of solutions ordered in decreasing function value. Note that SigOpt will not always return the number of solutions desired. Prior to completing the budget, SigOpt may not feel that it has sufficiently searched the space and may return less than num_solutions when calling Best Runs. After completing the budget, there will always be num_solutions entries in the Best Runs call.


  • budget must be set when a multi solution experiment is created
  • Multisolution supports finding up to 3 solutions i.e. num_solutions <= 3
  • Categorical parameters are not permitted
  • Multimetric Experiments are not permitted
  • Constraints are not permitted