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A globally convergent Newton method for convex SC1 minimization problems
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Outgoing claims: the papers this work cites, builds on, or argues with.
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A Trust Region Method for Constrained Nonsmooth Equations
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Lipschitzian inverse functions, directional derivatives, and applications inC 1,1 optimization
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Generalized Hessian matrix and second-order optimality conditions for problems withC 1,1 data
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A nonsmooth version of Newton's method
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Superlinearly convergent approximate Newton methods for LC1 optimization problems
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A Lagrangian finite generation technique for solving linear-quadratic problems in stochastic programming
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Piecewise Ck functions in nonsmooth analysis
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Newton's method for quadratic stochastic programs with recourse
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Constrained Optimization and Lagrange Multiplier Methods
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On Second-Order Sufficient Optimality Conditions for C 1,1 -Optimization Problems
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Semismooth and Semiconvex Functions in Constrained Optimization
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Minimization of Locally Lipschitzian Functions
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Nonsmooth Equations: Motivation and Algorithms
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Iterative Solution of Nonlinear Equations in Several Variables
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Optimization and Nonsmooth Analysis
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Newton's Method for B-Differentiable Equations
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Entropic Proximal Mappings with Applications to Nonlinear Programming
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Convergence Analysis of Some Algorithms for Solving Nonsmooth Equations
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Piecewise Smoothness, Local Invertibility, and Parametric Analysis of Normal Maps
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EXTENSION OF NEWTON AND QUASI-NEWTON METHODS TO SYSTEMS OF PC^1 EQUATIONS
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Iterative Solution of Nonlinear Equations in Several Variables
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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