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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 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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...