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In this work and the supporting Part II [1], we examine the performance of stochastic sub-gradient learning strategies under weaker conditions than usually considered in the literature. The new conditions are shown to be automatically satisfied by several ...
This technical note discusses convergence conditions of a generalized variant of primal-dual interior point methods. The generalization arises due to the permitted case of having a non-uniform complementarity perturbation vector, which is equivalent to hav ...
We propose a new proximal, path-following framework for a class of---possibly non-smooth---constrained convex problems. We consider settings where the non-smooth part is endowed with a proximity operator, and the constraint set is equipped with a self-conc ...
Mapping with Micro Aerial Vehicles (MAVs whose weight does not exceed 5 kg) is gaining importance in applications, such as corridor mapping, road and pipeline inspections, or mapping of large areas with homogeneous surface structure, e.g. forest or agricul ...
This paper considers a fundamental class of convex matrix optimization problems with low-rank solutions. We show it is possible to solve these problem using far less memory than the natural size of the decision variable when the problem data has a concise ...
We present a novel method for convex unconstrained optimization that, without any modifications, ensures: (i) accelerated convergence rate for smooth objectives, (ii) standard convergence rate in the general (non-smooth) setting, and (iii) standard converg ...
This paper describes synthesis of controllers involving Quadratic Programming (QP) optimization problems for control of nonlinear systems. The QP structure allows an implementation of the controller as a piecewise affine function, pre-computed offline, whi ...
Linear programming (LP) has played a key role in the study of algorithms for combinatorial optimization problems. In the field of approximation algorithms, this is well illustrated by the uncapacitated facility location problem. A variety of algorithmic me ...
Society for Industrial and Applied Mathematics2017
This paper presents a coordinated primal-dual interior point (PDIP) method for solving structured convex linear and quadratic programs (LP-QP) in a distributed man- ner. The considered class of problems represents a multi-agent setting, where the aggregate ...
We propose a computational approach to approximate the value function and control poli- cies for a finite horizon stochastic reach-avoid problem as follows. First, we formulate an infinite dimensional linear program whose solution characterizes the optimal ...