- High|dimensional optimization under nonconvex excluded volume ...🔍
- High dimensional optimization under non|convex excluded volume ...🔍
- [PDF] High|dimensional optimization under nonconvex excluded ...🔍
- Scalable Nonconvex Optimization Algorithms🔍
- Accelerated high|dimensional global optimization🔍
- Large|Scale Non|convex Stochastic Constrained Distributionally ...🔍
- From High Dimensional Statistics to Nonconvex Optimization – START🔍
- A two|level distributed algorithm for nonconvex constrained ...🔍
High|dimensional optimization under nonconvex excluded volume ...
High-dimensional optimization under nonconvex excluded volume ...
We consider high-dimensional random optimization problems where the dynamical variables are subjected to nonconvex excluded volume ...
High dimensional optimization under non-convex excluded volume ...
Title:High dimensional optimization under non-convex excluded volume constraints ... Abstract:We consider high dimensional random optimization ...
High-dimensional optimization under nonconvex excluded volume ...
High-dimensional optimization under nonconvex excluded volume constraints. Antonio Sclocchi 1 and Pierfrancesco Urbani 2. 1Institute of ...
(PDF) High dimensional optimization under non-convex excluded ...
PDF | We consider high dimensional random optimization problems where the dynamical variables are subjected to non-convex excluded volume constraints.
[PDF] High-dimensional optimization under nonconvex excluded ...
This work derives the results using the replica method and analyzes a dynamical algorithm, the Karush-Kuhn-Tucker algorithm, through dynamical mean-field ...
High-dimensional optimization under nonconvex excluded volume ...
We consider high-dimensional random optimization problems where the dynamical variables are subjected to nonconvex excluded volume constraints.
High-dimensional optimization under nonconvex excluded volume ...
High-dimensional optimization under nonconvex excluded volume constraints · List of references · Publications that cite this publication.
Scalable Nonconvex Optimization Algorithms: Theory and Applications
convergence rate results even for nonconvex problems in high-dimensional settings. Moreover, under some regularity conditions, the sequence of iterates in ...
Accelerated high-dimensional global optimization: A particle swarm ...
The curse of dimensionality often results in either premature convergence or slow convergence in high-dimensional global optimization.
Large-Scale Non-convex Stochastic Constrained Distributionally ...
Thus the distributions in the uncertainty set can be parameterized by an N-dimensional vector (Namkoong and Duchi 2016). Then the DRO problem becomes a min-max ...
From High Dimensional Statistics to Nonconvex Optimization – START
Next Generation Data Science: From High Dimensional Statistics to Nonconvex Optimization ... With the ever-increasing dimension, volume, and ... in highly nonconvex ...
A two-level distributed algorithm for nonconvex constrained ...
ADMM was proposed by Glowinski and Marrocco [18] and Gabay and Mercier [17] in the mid-1970s, and has deep roots in maximal monotone operator ...
Antonio Sclocchi - Google Scholar
High-dimensional optimization under nonconvex excluded volume constraints. A Sclocchi, P Urbani. Physical Review E 105 (2), 024134, 2022. 13, 2022. Failure and ...
Antonio Sclocchi - Google Scholar
High-dimensional optimization under nonconvex excluded volume constraints. A Sclocchi, P Urbani. Physical Review E 105 (2), 024134, 2022. 11, 2022. Failure and ...
Nonconvex Optimization in Machine Learning - OhioLINK ETD Center
rapidly growing size in both model dimension and sample volume, there is a strong ... An intrinsic quantity that affects the optimization ... in which S excludes ...
Global optimization of nonconvex NLPs and MINLPs with ...
Volume 19, Issue 5, May 1995, Pages 551-566. Computers & Chemical Engineering. Global optimization of nonconvex NLPs and MINLPs with applications in process ...
Understanding generalization error of SGD in nonconvex optimization
The success of deep learning has led to a rising interest in the generalization property of the stochastic gradient descent (SGD) method, ...
Convergence of Cubic Regularization for Nonconvex Optimization ...
Moreover, we show that the obtained asymptotic convergence rates of CR are order-wise faster than those of first-order gradient descent algorithms under the KŁ ...
Complexities in Projection-Free Stochastic Non-convex Minimization
analysis, higher LO complexity (O(1/4) in general) is ... errors in their proof and exclude it from comparison. ... In CVPR, volume 20, page 15, 2015. Kenneth L ...
Nonconvex Optimization Meets Low-Rank Matrix Factorization
This means that with high probability, the entire GD trajectory lies within a nice region that enjoys desired strong convexity and smoothness, thus enabling ...