Add to ORKGOracle Based Optimization (OBO) conveniently designates an approach to handle a class of convex optimization problems in which the information pertaining to the function to be minimized and/or to the feasible set takes the form of a linear outer approximation revealed by an oracle. We show, through three representative examples, how difficult problems can be cast in this format, and solved. We present an efficient method, Proximal-ACCPM, to trigger the OBO approach and give a snapshot on numerical results. This paper summarizes several contributions with the OBO approach and aims to give, in a single report, enough information on the method and its implementation to facilitate new applications
Proximal distance algorithms combine the classical penalty method of constrained minimization with d...
Robust optimization is a common optimization framework under uncertainty when problem parameters are...
Proximal methods are an important class of algorithms for solving nonsmooth, constrained, large-scal...
International audienceClassical First Order methods for large-scale convex-concave saddle point prob...
We give a simple and natural method for computing approximately optimal solutions for minimizing a c...
Convex optimization has developed a wide variety of useful tools critical to many applications in ma...
This paper demonstrates a customized application of the classical proximal point algorithm (PPA) to ...
In this paper, we propose a new algorithm to speed-up the convergence of accel-erated proximal gradi...
Finding multiple solutions of non-convex optimization problems is a ubiquitous yet challenging task....
Locating proximal points is a component of numerous minimization algorithms. This work focuses on de...
The rapid growth in data availability has led to modern large scale convex optimization problems tha...
We seek to solve convex optimization problems in composite form: minimize x∈Rn f(x): = g(x) + h(x), ...
Thesis (Ph.D.)--University of Washington, 2017Convex optimization is more popular than ever, with ex...
International audienceThe standard algorithms for solving large-scale convex–concave saddle point pr...
We provide a framework for computing the exact worst-case performance of any algorithm belonging to ...
Proximal distance algorithms combine the classical penalty method of constrained minimization with d...
Robust optimization is a common optimization framework under uncertainty when problem parameters are...
Proximal methods are an important class of algorithms for solving nonsmooth, constrained, large-scal...
International audienceClassical First Order methods for large-scale convex-concave saddle point prob...
We give a simple and natural method for computing approximately optimal solutions for minimizing a c...
Convex optimization has developed a wide variety of useful tools critical to many applications in ma...
This paper demonstrates a customized application of the classical proximal point algorithm (PPA) to ...
In this paper, we propose a new algorithm to speed-up the convergence of accel-erated proximal gradi...
Finding multiple solutions of non-convex optimization problems is a ubiquitous yet challenging task....
Locating proximal points is a component of numerous minimization algorithms. This work focuses on de...
The rapid growth in data availability has led to modern large scale convex optimization problems tha...
We seek to solve convex optimization problems in composite form: minimize x∈Rn f(x): = g(x) + h(x), ...
Thesis (Ph.D.)--University of Washington, 2017Convex optimization is more popular than ever, with ex...
International audienceThe standard algorithms for solving large-scale convex–concave saddle point pr...
We provide a framework for computing the exact worst-case performance of any algorithm belonging to ...
Proximal distance algorithms combine the classical penalty method of constrained minimization with d...
Robust optimization is a common optimization framework under uncertainty when problem parameters are...
Proximal methods are an important class of algorithms for solving nonsmooth, constrained, large-scal...