Found 1602 results, showing the newest relevant preprints. Sort by relevancy only.Update me on new preprints

Nonsmooth Optimization over Stiefel Manifold: Riemannian Subgradient Methods

We consider a class of nonsmooth optimization problems over Stefiel manifold, which are ubiquitous in engineering applications but still largely unexplored. Expand abstract.
28 days ago
6/10 relevant
arXiv

A Molecular Computing Approach to Solving Optimization Problems via Programmable Microdroplet Arrays

Combinatorial optimization problems are mapped to an Ising Hamiltonian and encoded in the form of intra- and inter- droplet interactions. Expand abstract.
28 days ago
10/10 relevant
chemRxiv

Sufficient optimality conditions in bilevel programming

This paper is concerned with the derivation of first- and second-order sufficient optimality conditions for optimistic bilevel optimization problems involving smooth functions. Expand abstract.
35 days ago
6/10 relevant
arXiv

A fast two-point gradient algorithm based on sequential subspace optimization method for nonlinear ill-posed problems

In this paper, we propose and analyze a fast two-point gradient algorithm for solving nonlinear ill-posed problems, which is based on the sequential subspace optimization method. Expand abstract.
35 days ago
4/10 relevant
arXiv

Robust Control Optimization for Quantum Approximate Optimization Algorithm

We examine the robustness of the quantum approximate optimization algorithm (QAOA), which can be used to solve certain quantum control problems, state preparation problems, and combinatorial optimization problems. Expand abstract.
38 days ago
4/10 relevant
arXiv

Training Neural Networks for Likelihood/Density Ratio Estimation

The main purpose of this work is to offer a simple and unified methodology for defining such optimization problems with guarantees that the solution is indeed the desired function. Expand abstract.
39 days ago
5/10 relevant
arXiv

Min-Max-Min Robustness for Combinatorial Problems with Discrete Budgeted Uncertainty

While the classical min-max problem with budgeted uncertainty is essentially as easy as the underlying deterministic problem, it turns out that the min-max-min problem is NPhard for many easy combinatorial optimization problems, and not approximable in general. Expand abstract.
43 days ago
5/10 relevant
arXiv

A Framework for Data-Driven Computational Mechanics Based on Nonlinear Optimization

This is achieved by the formulation of an approximate nonlinear optimization problem, which can be robustly solved, is computationally efficient, and does not rely on any special functional structure of the reconstructed constitutive manifold. Expand abstract.
43 days ago
4/10 relevant
arXiv

Solving Optimization Problems through Fully Convolutional Networks: an Application to the Travelling Salesman Problem

Is there any efficient way to solve certain optimization problem through deep learning? Expand abstract.
44 days ago
9/10 relevant
arXiv

Convergent Policy Optimization for Safe Reinforcement Learning

For such a problem, we construct a sequence of surrogate convex constrained optimization problems by replacing the nonconvex functions locally with convex quadratic functions obtained from policy gradient estimators. Expand abstract.
45 days ago
7/10 relevant
arXiv