针对无导数随机函数与确定性等式约束的自适应采样信赖域优化
Adaptive Sampling Trust Region Optimization for Derivative-free Stochastic Functions and Deterministic Equality Constraints
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中文总结 AI 辅助
针对含噪声零阶目标与可求导确定性等式约束的优化问题，提出ASTRO-DF的受限变体，证明其几乎必然收敛并在等式约束随机活动网络问题中取得数值结果。
中文摘要 AI 辅助
我们研究含噪声零阶目标观测值与可求导确定性非线性等式约束的优化问题。提出自适应采样信赖域无导数优化算法ASTRO-DF的受限变体，该方法在移动信赖域内的插值点处，由估计的目标值构建二次局部模型，并基于线性化约束通过类SQP框架的Byrd--Omojokun复合步骤提升可行性。我们采用新的受限临界性检验证明几乎必然收敛，并给出等式约束随机活动网络问题的数值结果。
英文摘要
We study optimization problems with noisy zeroth-order objective observations and deterministic nonlinear equality constraints with available derivatives. We propose a constrained variant of the adaptive-sampling trust-region derivative-free optimization algorithm---ASTRO-DF. The method builds quadratic local models from estimated objective values at interpolation points within a moving trust region and promotes feasibility through a Byrd--Omojokun composite-step based on linearized constraints, following an SQP-like framework. We prove almost sure convergence using a new constrained criticality test and present numerical results on an equality-constrained stochastic activity network problem.
