Framework transforms complex chance-constrained problems into convex SOCPs for individual constraints and uses copulas for joint constraints under moment, support, and data-driven ambiguity sets, demonstrated on beamforming.
Chance-constrained programming
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Unified framework for complex zero-sum games with chance constraints that converts probabilistic constraints into convex second-order cone programs under various distribution assumptions.
A hybrid method of oracle-guided gradient descent and interval arithmetic generates increasingly tight certified lower bounds on the maximum satisfaction probability for stochastic constraints.
A distribution-agnostic robust trajectory optimization framework uses chance-constrained reinforcement learning with rollout-based quantiles to enforce probabilistic feasibility on nominal trajectories via affine corrections.
A Bayesian framework learns uncertainties from data to generate robust multi-topology express network designs that reduce tail delivery risks at modest extra cost in simulations.
citing papers explorer
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Distributionally Robust Complex Chance-Constrained Optimization
Framework transforms complex chance-constrained problems into convex SOCPs for individual constraints and uses copulas for joint constraints under moment, support, and data-driven ambiguity sets, demonstrated on beamforming.
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Robust Chance Constrained Complex Zero-Sum Games
Unified framework for complex zero-sum games with chance constraints that converts probabilistic constraints into convex second-order cone programs under various distribution assumptions.
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Solving Stochastic Constraints by Oracle-based Gradient Descent and Interval Arithmetic
A hybrid method of oracle-guided gradient descent and interval arithmetic generates increasingly tight certified lower bounds on the maximum satisfaction probability for stochastic constraints.
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Distribution-Agnostic Robust Trajectory Optimization via Chance-Constrained Reinforcement Learning
A distribution-agnostic robust trajectory optimization framework uses chance-constrained reinforcement learning with rollout-based quantiles to enforce probabilistic feasibility on nominal trajectories via affine corrections.
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Bayesian Multi-Topology Express Transportation Network Design under Posterior Predictive Demand, Sorting-Efficiency and Delivery-Time Uncertainty
A Bayesian framework learns uncertainties from data to generate robust multi-topology express network designs that reduce tail delivery risks at modest extra cost in simulations.