The fast-online linear programming algorithm uses the fast dual iterative algorithm and applies a “boosting” strategy by running K rounds of random permutation. This algorithm can approximate the ...

It utilizes Python and the module CvxPy, as a modeling language for convex optimization problems. The chapter presents the implementation of linear and quadratic programming models ... matrices that ...

Implementation of the experiments presented in https://arxiv.org/abs/2009.05182. Example of trajectory for an uncertain 5D Dubins car model navigating in a cluttered ...

This paper presents a new heuristic to linearise the convex quadratic programming problem. The usual Karush-Kuhn-Tucker conditions are used but in this case a linear objective function is also ...

Abstract: Blocking filters are commonly used in array processing to excise targets from data when suitable target-free training data is not available. A drawback is the reduction of the effective size ...

Linear Programs (LPs) and Semidefinite Programs (SDPs) are central tools in the design and analysis of algorithms. In this course, we will study the mathematical foundations behind these convex ...

In this paper, we describe a new primal-dual path-following method to solve a convex ... by Darvay for linear programs. We prove that the short-update algorithm finds an epsilon-solution of (QP) in a ...

ABSTRACT: The Kuhn-Tucker theorem in nondifferential form is a well-known classical optimality criterion for a convex programming problems which is ... The major distinctive property of the ...

Convex Minimum Cost Flow: from Linear Programming to Accelerated Dual Descent Method ... which is often an integration of texture properties along the light/x-ray path. Since most of the objects have ...