1 citations · 1 across the 15 of their papers we have counts for
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Verification of Sequential Convex Programming for Parametric Non-convex Optimization
Rajiv Sambharya, Nikolai Matni, George Pappas
We introduce a verification framework to exactly verify the worst-case performance of sequential convex programming (SCP) algorithms for parametric non-convex optimization. The ver…
Learning Acceleration Algorithms for Fast Parametric Convex Optimization with Certified Robustness
Rajiv Sambharya, Jinho Bok, Nikolai Matni +1
We develop a machine-learning framework to learn hyperparameter sequences for accelerated first-order methods (e.g., the step size and momentum sequences in accelerated gradient de…
Learning with Imperfect Models: When Multi-step Prediction Mitigates Compounding Error
Anne Somalwar, Bruce D. Lee, George J. Pappas +1
Compounding error, where small prediction mistakes accumulate over time, presents a major challenge in learning-based control. For example, this issue often limits the performance…
Policy Gradient for LQR with Domain Randomization
Tesshu Fujinami, Bruce D. Lee, Nikolai Matni +1
Domain randomization (DR) enables sim-to-real transfer by training controllers on a distribution of simulated environments, with the goal of achieving robust performance in the rea…
Domain Randomization is Sample Efficient for Linear Quadratic Control
Tesshu Fujinami, Bruce D. Lee, Nikolai Matni +1
We study the sample efficiency of domain randomization and robust control for the benchmark problem of learning the linear quadratic regulator (LQR). Domain randomization, which sy…