120 citations · 370 across the 36 of their papers we have counts for
7 papers · 1 filter
Using affine policies to reformulate two-stage Wasserstein distributionally robust linear programs to be independent of sample size
Youngchae Cho, Insoon Yang
Intensively studied in theory as a promising data-driven tool for decision-making under ambiguity, two-stage distributionally robust optimization (DRO) problems over Wasserstein ba…
Wasserstein Distributionally Robust Control of Partially Observable Linear Stochastic Systems
Astghik Hakobyan, Insoon Yang
Distributionally robust control (DRC) aims to effectively manage distributional ambiguity in stochastic systems. While most existing works address inaccurate distributional informa…
Anderson Acceleration for Partially Observable Markov Decision Processes: A Maximum Entropy Approach
Mingyu Park, Jaeuk Shin, Insoon Yang
Partially observable Markov decision processes (POMDPs) is a rich mathematical framework that embraces a large class of complex sequential decision-making problems under uncertaint…
On representation formulas for optimal control: A Lagrangian perspective
Yeoneung Kim, Insoon Yang
In this paper, we study representation formulas for finite-horizon optimal control problems with or without state constraints, unifying two different viewpoints: the Lagrangian and…
Wasserstein Distributionally Robust Control of Partially Observable Linear Systems: Tractable Approximation and Performance Guarantee
Astghik Hakobyan, Insoon Yang
Wasserstein distributionally robust control (WDRC) is an effective method for addressing inaccurate distribution information about disturbances in stochastic systems. It provides v…
On Affine Policies for Wasserstein Distributionally Robust Unit Commitment
Youngchae Cho, Insoon Yang
This paper proposes a unit commitment (UC) model based on data-driven Wasserstein distributionally robust optimization (WDRO) for power systems under uncertainty of renewable gener…
Accelerated Gradient Methods for Geodesically Convex Optimization: Tractable Algorithms and Convergence Analysis
Jungbin Kim, Insoon Yang
We propose computationally tractable accelerated first-order methods for Riemannian optimization, extending the Nesterov accelerated gradient (NAG) method. For both geodesically co…