- COMPLEXITY OF A QUADRATIC PENALTY ACCELERATED ...🔍
- Global Optimization for Nonconvex Programs via Convex Proximal ...🔍
- Recent Advances in Nonconvex Methods for High|Dimensional ...🔍
- Nonconvex Optimization and Its Applications🔍
- Inexact Nonconvex Newton|Type Methods🔍
- Convexity in Non|convex Optimizations of Streaming Applications🔍
- A BFGS|SQP method for nonsmooth🔍
- Online non|convex optimization with imperfect feedback🔍
[PDF] High|dimensional optimization under nonconvex excluded ...
COMPLEXITY OF A QUADRATIC PENALTY ACCELERATED ... - CDN
The second subsection is devoted to the study of a general inexact proximal point framework to solve nonconvex optimization problems. In this sub- section ...
Thus our proximal point method does not exclude local minimizers. Theorem 2 (Basic convergence theorem.) Under our four assumptions and the se- quence generated ...
Recent Advances in Nonconvex Methods for High-Dimensional ...
successful for solving nonconvex problems in practice. 7. Page 12. Why? 8. Page 13. Nonconvex optimization with performance guarantees. Phase retrieval ...
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 ...
Inexact Nonconvex Newton-Type Methods - UC Berkeley Statistics
example, for finite-sum problems in high dimensions in which. F ... non-convex optimization under inexact Hessian information. Math. Pro ...
Convexity in Non-convex Optimizations of Streaming Applications
In convex decomposition, we decompose the optimization problem to exclude the convex variable and solve for the remaining variables as a new optimization.
A BFGS-SQP method for nonsmooth, nonconvex, constrained ...
the inherent high degree of nonsmoothness in the optimization problem. The constraint plant also shows activities, with the moduli of ...
Online non-convex optimization with imperfect feedback - Hal-Inria
In the finite-dimensional case, this regularizer forms the basis of ... Learning in non-convex games with an optimization oracle. In ...
(PDF) A quantum-classical performance separation in nonconvex ...
... in the empirical test is unlikely to yield any superpolynomial oracle separation directly. This is because our optimization instances are d-dimensional ...
A flexible framework for cubic regularization algorithms for non ...
Keywords: non-convex optimization, optimization in ... interior-point algorithms for infinite-dimensional nonconvex minimization subject to.
Derivative-Free Optimization of High-Dimensional Non-Convex ...
We then propose the sequential random embeddings (SRE) to overcome the embedding gap. SRE applies the random embedding several times sequentially, and in each ...
Robust Optimization With Simulated Annealing | PDF - Scribd
... nonconvex cost function as well as nonconvex ... design x, the robust optimization method seeks to iteratively update x in order to exclude
A nonconvex cost optimization approach to tracking multiple targets
The problem of tracking multiple targets in the presence of displacement noise and clutter is formulated as a nonconvex optimisation problem.
Convergence of Optimization Algorithms on Non-Convex Functions
The paper "Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak- Lojasiewicz Condition"(https://arxiv.org/pdf/ ...
Solving non-convex optimization with global optimization algorithm ...
In fact M^n~M for any finite n and any infinite set M. So the fact that you problem has one dimension is nothing. It is still hard as problem ...
Non-Convex Optimization in Networks and High-Dimensional ...
In this thesis, we consider two sub-fields of Mathematics, on the one hand Mathematics of Data Science, on the other hand High-Dimensional Probability.
... Optimization algorithm to efficiently tackle high-dimensional problems through kernel selection. We use PID controller optimization in drones as a ...
Machine Learning | Cool Papers - Immersive Paper Discovery
Despite the development of numerous variance reduction algorithms in the past decade aimed at accelerating stochastic optimization in both convex and nonconvex ...