High|dimensional optimization under nonconvex excluded volume ...

11 On Nonconvex Optimization for Machine Learning: Gradients ...

Prior work shows that stochastic gradient descent converges to first-order stationary points in a number of iterations that is independent of dimension. Theorem ...

Nonconvex optimization for optimum retrieval of the transmission ...

Here, we propose a modified nonconvex optimization approach. Through numerical evaluations, it shows that the optimum focusing efficiency is approached with ...

Trends in Large-scale Nonconvex Optimization - YouTube

Suvrit Sra, MIT https://simons.berkeley.edu/talks/suvrit-sra-10-05-17 Fast Iterative Methods in Optimization.

Optimizing Non-Intersecting Synthetic Vascular Trees in Nonconvex ...

However, most algorithms are limited to single trees inside convex perfusion volumes. We introduce a new framework for generating multiple trees ...

Gradients, Stochasticity, and Saddle Points - On Nonconvex ...

More recent theory has shown that GD and SGD can avoid saddle points, but the dependence on dimension in these analyses is polynomial. For modern machine ...

Global Convergence of ADMM in Nonconvex Nonsmooth Optimization

asymptotically “steep” in the exclusion set SM . Such functions ... In: Computer graphics forum, vol. 32, pp. 113–123. Wiley Online ...

High dimensional optimization under non-convex excluded volume ...

We consider high dimensional random optimization problems where the dynamical variables are subjected to non-convex excluded volume constraints.

An adaptive high order method for finding third-order critical points ...

Journal of Global Optimization, Volume 84, Issue 2. Pages 369 - 392. https ... higher order saddle points in non-convex optimization. In: Conference on ...

Nonconvex Optimization for High Dimensional Learning

While nonconvex optimization problems are known to be intractable in general, simple local search heuristics such as (stochastic) gradient descent are often ...

Second Order and High Order Approaches for Nonconvex ...

This thesis explores two main lines of research in the field of nonconvex optimization with a narrow focus on second and higher order methods.

An adaptive high order method for finding third-order critical points ...

Downloadable (with restrictions)! Recently, the optimization methods for computing higher-order critical points of nonconvex problems attract growing ...

Robust Optimization for Unconstrained Simulation-based Problems

The corresponding objective function is highly nonconvex and resides in a 100-dimensional design space. Starting from an “optimized” design, we report a ...

Dynamic Regret Bounds for Online Nonconvex Optimization

Foundations and Trends in Optimization, 2015, vol. 2, no. 4. [9] E. C. Hall and R. M. Willett, “Online convex optimization in dynamic environments,” IEEE Jrnl.

SciPost Phys. 10, 013 (2021) - Proliferation of non-linear excitations ...

... optimization problem that has been recently investigated in [1]. ... , High-dimensional optimization under nonconvex excluded volume constraints

Nonconvex Optimization and Its Applications - download

The present lecture note is dedicated to the study of the optimality conditions and the duality results for nonlinear vector optimization problems, in ...

Large-scale Non-convex Stochastic Constrained Distributionally ...

In this way, the optimization problem under an unknown distribution is rewritten into one ... Introductory lectures on convex optimization: A basic course, volume ...

Limiting Behaviors of Nonconvex-Nonconcave Minimax ...

Robust optimization, volume 28. Princeton University Press, 2009 ... What is local optimality in nonconvex- nonconcave minimax optimization?, 2020.

High Probability Bounds for a Class of Nonconvex Algorithms with...

Summary Of The Paper: This paper proposed a new analysis for AdaGrad method in smooth and non-convex optimization, to get high probability convergence toward ...

Non-convex Optimization for Machine Learning - Prateek Jain

A vast majority of machine learning algorithms train their models and perform inference by solving optimization problems. In order to ...

Nonconvex Optimization for High-dimensional Learning - YouTube

Fast Iterative Methods in Optimization. Nonconvex Optimization for High-dimensional Learning: From ReLUs to Submodular Maximization. 2.7K ...