Found 1728 results, showing the newest relevant preprints. Sort by relevancy only.Update me on new preprints

Introduction to Nonsmooth Analysis and Optimization

These notes aim to give an introduction to generalized derivative concepts useful in deriving necessary optimality conditions and numerical algorithms for infinite-dimensional nondifferentiable optimization problems that arise in inverse problems, imaging, and PDE-constrained optimization. Expand abstract.
16 days ago
5/10 relevant
arXiv

Model Inversion Networks for Model-Based Optimization

We evaluate MINs on tasks from the Bayesian optimization literature, high-dimensional model-based optimization problems over images and protein designs, and contextual bandit optimization from logged data. Expand abstract.
17 days ago
7/10 relevant
arXiv

Stochastic Recursive Variance Reduction for Efficient Smooth Non-Convex Compositional Optimization

Stochastic compositional optimization arises in many important machine learning tasks such as value function evaluation in reinforcement learning and portfolio management. Expand abstract.
17 days ago
5/10 relevant
arXiv

FEM Based Preliminary Design Optimization in Case of Large Power Transformers

Due to the complexity of this task, it belongs to the most general branch of discrete, non-linear mathematical optimization problems. Expand abstract.
17 days ago
4/10 relevant
Preprints.org

Nearly optimal first-order methods for convex optimization under gradient norm measure: An adaptive regularization approach

In the development of first-order methods for smooth (resp., composite) convex optimization problems minimizing smooth functions, the gradient (resp., gradient mapping) norm is a fundamental optimality measure for which a regularization technique of first-order methods is known to be nearly optimal. Expand abstract.
21 days ago
4/10 relevant
arXiv

What do QAOA energies reveal about graphs?

Quantum Approximate Optimization Algorithm (QAOA) is a hybrid classical-quantum algorithm to approximately solve NP optimization problems such as MAX-CUT. Expand abstract.
21 days ago
4/10 relevant
arXiv

Upper and Lower Bounds for Large Scale Multistage Stochastic Optimization Problems: Decomposition Methods

To tackle such large scale problems, we propose two decomposition methods, whether handling the coupling constraints by prices or by resources. Expand abstract.
25 days ago
9/10 relevant
arXiv

Upper and Lower Bounds for Large Scale Multistage Stochastic Optimization Problems: Application to Microgrid Management

Moreover, the decomposition methods are much faster than the SDDP method in terms of computation time, thus allowing to tackle problem instances incorporating more than 60 state variables in a Dynamic Programming framework. Expand abstract.
25 days ago
7/10 relevant
arXiv

Convolutional Neural Network-based Topology Optimization (CNN-TO) By Estimating Sensitivity of Compliance from Material Distribution

This paper proposes a new topology optimization method that applies a convolutional neural network (CNN), which is one deep learning technique for topology optimization problems. Expand abstract.
25 days ago
7/10 relevant
arXiv

Distributed Online Optimization with Long-Term Constraints

We consider distributed online convex optimization problems, where the distributed system consists of various computing units connected through a time-varying communication graph. Expand abstract.
28 days ago
7/10 relevant
arXiv