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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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Journal ArticleDOI
Branch-and-cut for linear programs with overlapping SOS1 constraints
Tobias Fischer,Marc E. Pfetsch +1 more
TL;DR: This article investigates a branch-and-cut algorithm to solve linear programs with SOS1 constraints and demonstrates the effectiveness of this approach by comparing it to the solution of a mixed-integer programming formulation, if the variables appearing in SOS1 constraint ar bounded.
Book ChapterDOI
Efficient Contact Mode Enumeration in 3D.
TL;DR: The goal of this paper is to provide a novel computational tool for researchers to use to simulate, analyze, and control robotic systems that make and break contact with the environment by developing the first efficient 3D contact mode enumeration algorithm.
Journal ArticleDOI
Two class of synchronous matrix multisplitting schemes for solving linear complementarity problems
Mehdi Dehghan,Masoud Hajarian +1 more
TL;DR: By applying the generalized accelerated overrelaxation (GAOR) and the symmetric successive overrelAXation (SSOR) techniques, two class of synchronous matrix multisplitting methods to solve LCP (M,q) are introduced.
Journal ArticleDOI
Global error bounds for monotone affine variational inequality problems
TL;DR: In this paper, a global gradient projection type error bound for monotone affine variational inequality problems is given, which is based on a previously obtained global error bound based on gradient projection for linear complementarity problems.
Journal ArticleDOI
On the equivalence between some projected and modulus-based splitting methods for linear complementarity problems
TL;DR: This paper shows that some well-known projected splitting methods are equivalent, iteration by iteration, to some (accelerated) modulus-based matrix splitting methods with a specific choice of the parameter $${\varOmega }$$ and generalizes this result to any $$ varOmega $ by formulating new classes of projected splitting Methods.