4 papers · 1 filter
Stochastic LQR Design With Disturbance Preview
Jietian Liu, Laurent Lessard, Peter Seiler
This paper considers the discrete-time, stochastic LQR problem with steps of disturbance preview information where is finite. We first derive the solution for this problem…
Adaptive Acceleration Without Strong Convexity Priors Or Restarts
Joao V. Cavalcanti, Laurent Lessard, Ashia C. Wilson
A longstanding challenge in optimization is achieving optimal performance when the strong convexity parameter m is unknown. In this paper, we propose NAG-free, a simple extension o…
The Fastest Known First-Order Method for Minimizing Twice Continuously Differentiable Smooth Strongly Convex Functions
Bryan Van Scoy, Laurent Lessard
We consider iterative gradient-based optimization algorithms applied to functions that are smooth and strongly convex. The fastest globally convergent algorithm for this class of f…
Adaptive Backtracking Line Search
Joao V. Cavalcanti, Laurent Lessard, Ashia C. Wilson
Backtracking line search is foundational in numerical optimization. The basic idea is to adjust the step-size of an algorithm by a constant factor until some chosen criterion (e.g.…