From the 1 of 6 linked papers with an AI index.
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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…
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)}…
A consistently adaptive trust-region method
Fadi Hamad, Oliver Hinder
Adaptive trust-region methods attempt to maintain strong convergence guarantees without depending on conservative estimates of problem properties such as Lipschitz constants. Howev…
The Price of Adaptivity in Stochastic Convex Optimization
Yair Carmon, Oliver Hinder
We prove impossibility results for adaptivity in non-smooth stochastic convex optimization. Given a set of problem parameters we wish to adapt to, we define a "price of adaptivity"…