Complementarity constraints are treated as a Lie group under relaxation to enable parameterization that satisfies them by construction in LCQP solvers.
qpOASES: a parametric active-set algorithm for quadratic programming
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
Reinforcement learning learns a policy that adapts control parameters of a regularized interior-point method, accelerating high-accuracy solutions for convex quadratic programs and generalizing across problem classes after lightweight training.
citing papers explorer
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Complementarity by Construction: A Lie-Group Approach to Solving Quadratic Programs with Linear Complementarity Constraints
Complementarity constraints are treated as a Lie group under relaxation to enable parameterization that satisfies them by construction in LCQP solvers.
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Reinforcement learning for adaptive interior point methods in convex quadratic programming
Reinforcement learning learns a policy that adapts control parameters of a regularized interior-point method, accelerating high-accuracy solutions for convex quadratic programs and generalizing across problem classes after lightweight training.