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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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Some perspectives on the analysis and control of complementarity systems
TL;DR: This paper is devoted to presenting controllability and stabilizability issues associated to a class of nonsmooth dynamical systems, namely complementarity dynamical Systems, which mainly focuses on mechanical applications.
Journal ArticleDOI
Interaction capture and synthesis
Paul G. Kry,Dinesh K. Pai +1 more
TL;DR: A novel position-based linear complementarity problem formulation is described that includes friction, breaking contact, and the compliant coupling between contacts at different fingers that is validated using data from previous work and the own perturbation-based estimates.
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A B-differentiable equation-based, globally and locally quadratically convergent algorithm for nonlinear programs, complementarity and variational inequality problems
TL;DR: The algorithm is based on a unified formulation of these three mathematical programming problems as a certain system of B-differentiable equations, and is a modification of the damped Newton method described in Pang (1990) for solving such systems of nonsmooth equations.
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An iterative approach for cone complementarity problems for nonsmooth dynamics
Mihai Anitescu,Alessandro Tasora +1 more
TL;DR: The method is an extension of the Gauss-Seidel andGauss-Jacobi method with overrelaxation for symmetric convex linear complementarity problems and is proved to be convergent under fairly standard assumptions.
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Further applications of a splitting algorithm to decomposition in variational inequalities and convex programming
TL;DR: This work shows that existing convergence results for this projection algorithm follow from one given by Gabay for a splitting algorithm for finding a zero of the sum of two maximal monotone operators, and obtains a decomposition method that can simultaneously dualize the linear constraints and diagonalize the cost function.