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This work concerns the local convergence theory of Newton and quasi-Newton methods for convex-composite optimization: minimize f(x):=h(c(x)), where h is an infinite-valued proper convex function and c is C^2-smooth.
A quadratically-convergent algorithm for general nonlinear programming problems
S. M. Robinson · 1972
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Perturbed kuhn-tucker points and rates of convergence for a class of nonlinear-programming algorithms
S. M. Robinson · 1974
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Algorithms for nonlinear constraints that use lagrangian functions
M. J. Powell · 1978
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A fast algorithm for nonlinearly constrained optimization calculations
M. J. Powell · 1978
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Strongly regular generalized equations
S. M. Robinson · 1980
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Descent methods for composite nondifferentiable optimization problems
J. V. Burke · 1985
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Extensions of subgradient calculus with applications to optimization
R. T. Rockafellar · 1985
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Local properties of algorithms for minimizing nonsmooth composite functions
R. S. Womersley · 1985
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On the superlinear convergence of a trust region algorithm for nonsmooth optimization
Y. Yuan · 1985
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Second order necessary and sufficient conditions for convex composite ndo
J. V. Burke · 1987
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Local properties of inexact methods for minimizing nonsmooth composite functions
S. J. Wright · 1987
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Second-order necessary conditions of the kuhn-tucker type under new constraint qualifications
H. Kawasaki · 1988
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Second-order optimality conditions in nonlinear programming obtained by way of epi-derivatives
R. T. Rockafellar · 1989
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Optimality conditions for non-finite valued convex composite functions
J. V. Burke and R. Poliquin · 1992
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Weak sharp minima in mathematical programming
J. V. Burke and M. C. Ferris · 1993
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Towards second-order methods for structured nonsmooth optimization
M. L. Overton and X. Ye · 1994
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A gauss—newton method for convex composite optimization
J. V. Burke and M. C. Ferris · 1995
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On uniqueness of lagrange multipliers in composite optimization
S. Deng · 1996
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Variational analysis
R. T. Rockafellar and R. J.-B. Wets · 1998
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Optimization viewpoint on kalman smoothing with applications to robust and sparse estimation
A. Aravkin, J. Burke, and G. Pillonetto · 2014
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Implicit functions and solution mappings
A. L. Dontchev and R. T. Rockafellar · 2014
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Clarke subgradients for directionally lipschitzian stratifiable functions
D. Drusvyatskiy, A. D. Ioffe, and A. S. Lewis · 2014
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Convex analysis
R. T. Rockafellar · 2015
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Strong metric subregularity of mappings in variational analysis and optimization
R. Cibulka, A. Dontchev, and A. Kruger · 2016
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On convergence rates of linearized proximal algorithms for convex composite optimization with applications
Y. Hu, C. Li, and X. Yang · 2016
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M. R. Osborne · 2001
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Active sets, nonsmoothness, and sensitivity
A. S. Lewis · 2002
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On convergence of the gauss-newton method for convex composite optimization
C. Li and X. Wang · 2002
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Identifying active constraints via partial smoothness and prox-regularity
W. Hare and A. S. Lewis · 2004
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Majorizing functions and convergence of the gauss–newton method for convex composite optimization
C. Li and K. Ng · 2007
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Sparse/robust estimation and kalman smoothing with nonsmooth log-concave densities: Modeling, computation, and theory
A. Aravkin, J. Burke, and G. Pillonetto · 2013
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A proximal method for composite minimization
A. S. Lewis and S. J. Wright · 2016
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The nonsmooth landscape of phase retrieval
D. Davis, D. Drusvyatskiy, and C. Paquette · 2017
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Stochastic methods for composite optimization problems
J. Duchi and F. Ruan · 2017
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Solving (most) of a set of quadratic equalities: Composite optimization for robust phase retrieval
J. C. Duchi and F. Ruan · 2017
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Composite difference-max programs for modern statistical estimation problems
Y. Cui, J.-S. Pang, and B. Sen · 2018
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Stochastic model-based minimization of weakly convex functions
D. Davis and D. Drusvyatskiy · 2018
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Error bounds, quadratic growth, and linear convergence of proximal methods
D. Drusvyatskiy and A. S. Lewis · 2018
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