A learning approach trains neural networks to approximate solutions of multiparametric GNEPs using NI gap loss with value surrogates, achieving large speedups and providing new existence conditions for continuous selections.
Input-to-State Stability of Newton Meth- ods in Nash Equilibrium Problems with Applications to Game-Theoretic Model Predictive Control
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DR-DAQP is a hybrid solver using operator splitting and active-set methods that solves affine variational inequalities exactly in finite time under specified conditions and runs up to two orders of magnitude faster than the PATH solver.
A multiparametric algorithm provides explicit solutions to finite and infinite-horizon constrained dynamic games, making game-theoretic MPC feasible for moderate-sized multi-agent systems at high sampling rates.
Time-distributed Newton iterations enable real-time Nash equilibrium seeking in potential-game GT-MPC for autonomous vehicles, as shown in intersection-crossing simulations.
citing papers explorer
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Learning Approximate Solutions to Multiparametric Generalized Nash Equilibrium Problems
A learning approach trains neural networks to approximate solutions of multiparametric GNEPs using NI gap loss with value surrogates, achieving large speedups and providing new existence conditions for continuous selections.
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\texttt{DR-DAQP}: An Hybrid Operator Splitting and Active-Set Solver for Affine Variational Inequalities
DR-DAQP is a hybrid solver using operator splitting and active-set methods that solves affine variational inequalities exactly in finite time under specified conditions and runs up to two orders of magnitude faster than the PATH solver.
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The explicit game-theoretic linear quadratic regulator for constrained multi-agent systems
A multiparametric algorithm provides explicit solutions to finite and infinite-horizon constrained dynamic games, making game-theoretic MPC feasible for moderate-sized multi-agent systems at high sampling rates.
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Real-Time Solution-Seeking for Game-Theoretic Autonomous Driving via Time-Distributed Iterations
Time-distributed Newton iterations enable real-time Nash equilibrium seeking in potential-game GT-MPC for autonomous vehicles, as shown in intersection-crossing simulations.