From the 1 of 6 linked papers with an AI index.
6 papers
An adaptive interior-point method with backtracking line search for convex constrained optimization
Fadi Hamad, Oliver Hinder
The paper proposes and analyzes an adaptive interior‑point algorithm that uses a regularized Newton step with backtracking line search on a log‑barrier function to solve convex pro…
Clipping the Price of Adaptivity at the Tail
Itai Kreisler, Yair Carmon, Oliver Hinder
Adaptive stochastic convex optimization (SCO) methods face a fundamental ``price of adaptivity'' barrier: under the standard set of assumptions, they cannot efficiently adapt to la…
The Sample Complexity of Parameter-Free Stochastic Convex Optimization
Jared Lawrence, Ari Kalinsky, Hannah Bradfield +2
We study the sample complexity of stochastic convex optimization when problem parameters such as the distance to optimality and the Lipschitz constant are unknown. We pursue two st…
PDLP: A Practical First-Order Method for Large-Scale Linear Programming
David Applegate, Mateo DÃaz, Oliver Hinder +4
We present PDLP, a practical first-order method for linear programming (LP) designed to solve large-scale LP problems. PDLP is based on the primal-dual hybrid gradient (PDHG) metho…
A simple and practical adaptive trust-region method
Fadi Hamad, Oliver Hinder
We present an adaptive trust-region method for unconstrained optimization that allows inexact solutions to the trust-region subproblems. Our method is a simple variant of the class…
Worst-case analysis of restarted primal-dual hybrid gradient on totally unimodular linear programs
Oliver Hinder
We analyze restarted PDHG on totally unimodular linear programs. In particular, we show that restarted PDHG finds an -optimal solution in $O( H m_1^{2.5} \sqrt{\textbf{nnz}(A)}…