Trust-Region Optimization
Overview
Trust-region optimization studies how to choose steps using a local approximation of an objective, accepting steps that improve the objective and adjusting the region when predictions are inaccurate. Methods include quadratic-model and sequential quadratic programming approaches, stochastic and derivative-free updates, and Bayesian optimization with local Gaussian-process models. These methods are used for constrained and noisy problems, expensive black-box search, and fine-tuning policies or machine-learning models; evaluations often include CUTEst problems, synthetic optimization benchmarks, and application-specific tasks. A central challenge is balancing reliable local progress and convergence guarantees against the need to scale, handle noisy evaluations, and explore beyond the current region.
Latest papers
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