Abstract: A system optimizes an objective function by iteratively refining the objective function. The system evaluates an initial set of results for a set of initial candidates according to an objective function. The system generates a local model. The system identifies a plurality of new candidates. The system determines, for each new candidate, a prediction of whether the candidate violates a constraint. The system selects a new candidate based on the prediction for each of the candidates and updates the local model using the new candidate. The system identifies a potential optimal solution from the updated local model. The system distributes resources of a computing system according to the potential optimal solution.

Abstract: A transport service system comprises a plurality of drivers and riders over a plurality of cities. A resource allocation function using a plurality of variables associated with incentives that may be provided to the drivers and riders over the cities with an output describing a profitability of the system. The system evaluates an initial set of results for an initial set of randomized candidates according to a resource allocation function. The system generates a plurality of local models. Each local model is a Gaussian process posterior distribution over a trust region centered around a randomized candidate. The system samples a function from each distribution and identifies a candidate with an optimal result according to the sampled function. The system evaluates the best candidate chosen from among the identified candidates with the resource allocation function. The system then identifies an optimal solution from the evaluations and distributes resources according to the optimal solution.