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Linear complementarity, linear and nonlinear programming
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The article was published on 1988-01-01 and is currently open access. It has received 1012 citations till now. The article focuses on the topics: Mixed complementarity problem & Complementarity theory.read more
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Globally Convergent Newton Methods for Nonsmooth Equations
TL;DR: These methods resemble the well-known family of damped Newton and Gauss-Newton methods for solving systems of smooth equations and generalize some recent Newton-like methods for solve B-differentiable equations which arise from various mathematical programs.
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A New Algorithm for State-Constrained Separated Continuous Linear Programs
Xiaodong Luo,Dimitris Bertsimas +1 more
TL;DR: In this article, a new class of continuous linear programming problems is proposed for solving large-scale problems in this class under mild assumptions on the form of the problem data, and the absence of a duality gap is shown.
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Fluid film lubrication in the presence of cavitation: a mass-conserving two-dimensional formulation for compressible, piezoviscous and non-Newtonian fluids
TL;DR: In this article, a mass-conserving formulation of the Reynolds equation has been proposed to solve textured bearing and squeeze problems in the presence of cavitation in a one dimensional domain for incompressible fluids, has been extended to include the effects of fluid compressibility, piezoviscosity and the non-Newtonian fluid behaviour and it has also applied to the analysis of two dimensional problems.
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Projection-free parallel quadratic programming for linear model predictive control
TL;DR: This paper introduces a projection-free iterative optimisation algorithm and discusses its application to linear MPC, and discusses how termination conditions with guaranteed degree of suboptimality can be enforced, and how the algorithm performance can be optimised by pre-computing the matrices in a parametric form.
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Weighted least-squares reconstruction methods for positron emission tomography
TL;DR: The algorithms used to minimize the WLS objective functions guarantee nonnegative estimates and they converged faster than the maximum likelihood expectation-maximization (ML-EM) algorithm and produced images that had significantly better resolution and contrast.