## Nonlinear optimization and applications |

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### Contents

Towards a discrete Newton method with memory for largescale optimization R | 1 |

Correction theorems for nonsmooth systems V F Demyanov | 5 |

On regularity for generalized systems and applications M Castellani G Mastrocni | 13 |

42 other sections not shown

### Common terms and phrases

algorithm analysis approximate assume assumption augmented Lagrangian function bounded compute cone consider continuously differentiable convex set defined definition denote derivatives DINEMO equivalent ergodic sequence exists feasible set Fukushima function evaluations gap function geometry Giannessi globally convergent gradient graph hence implies interior-point interpolation iteration Jacobian KKT pair L-BFGS Lagrangian Lemma limited memory line search linear complementarity problems linear programming Lipschitz continuous Lipschitz manifold Mangasarian mapping Math Mathematical Programming MATLAB matrix merit function method for solving monotone multifunction neural network Newton method node nonlinear complementarity problem nonlinear programming nonsmooth equations objective function obtained optimality conditions Optimization and Applications optimization method optimization problems parameter polyhedral Problem NLP Proof properties Proposition quadratic quasi-Newton methods Rockafellar satisfies search direction semismooth solution space transformation stationary point step stepsize subgradient superlinear convergence Theorem trust region unconstrained optimization variables variational inequality vector