Self-concordant inclusions: a unified framework for path-following generalized Newton-type algorithms
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Publication:2316618
DOI10.1007/s10107-018-1264-6zbMath1423.90188arXiv1707.07403OpenAlexW2964068842WikidataQ130047112 ScholiaQ130047112MaRDI QIDQ2316618
Tianxiao Sun, Shu Lu, Quoc Tran Dinh
Publication date: 6 August 2019
Published in: Mathematical Programming. Series A. Series B (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1707.07403
saddle-point problemsmonotone inclusionconstrained convex programminggeneralized Newton-type methodspath-following schemesself-concordant inclusion
Convex programming (90C25) Large-scale problems in mathematical programming (90C06) Computational methods for problems pertaining to operations research and mathematical programming (90-08)
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Cites Work
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- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- Gradient methods for minimizing composite functions
- SDPNAL+: a majorized semismooth Newton-CG augmented Lagrangian method for semidefinite programming with nonnegative constraints
- Efficient evaluation of scaled proximal operators
- Alternating direction augmented Lagrangian methods for semidefinite programming
- A primal-dual interior point method for nonlinear semidefinite programming
- A B-differentiable equation-based, globally and locally quadratically convergent algorithm for nonlinear programs, complementarity and variational inequality problems
- Dual extrapolation and its applications to solving variational inequalities and related problems
- Smoothing technique and its applications in semidefinite optimization
- Equivalent differentiable optimization problems and descent methods for asymmetric variational inequality problems
- On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
- A logarithmic-quadratic proximal method for variational inequalities
- Local analysis of Newton-type methods for variational inequalities and nonlinear programming
- Newton's method for a class of nonsmooth functions
- A semismooth equation approach to the solution of nonlinear complementarity problems
- A feasible semismooth asymptotically Newton method for mixed complementarity problems
- Introductory lectures on convex optimization. A basic course.
- A hybrid approximate extragradient-proximal point algorithm using the enlargement of a maximal monotone operator
- Some recent advances in projection-type methods for variational inequalities
- A first-order primal-dual algorithm for convex problems with applications to imaging
- Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm
- A nonsmooth version of Newton's method
- Lectures on Modern Convex Optimization
- On the Implementation and Usage of SDPT3 – A Matlab Software Package for Semidefinite-Quadratic-Linear Programming, Version 4.0
- Proximal Splitting Methods in Signal Processing
- Achieving Exact Cluster Recovery Threshold via Semidefinite Programming
- Robust Stochastic Approximation Approach to Stochastic Programming
- Strongly Regular Generalized Equations
- Applications of a Splitting Algorithm to Decomposition in Convex Programming and Variational Inequalities
- Global Convergence of Damped Newton's Method for Nonsmooth Equations via the Path Search
- Self-Scaled Barriers and Interior-Point Methods for Convex Programming
- Variational Analysis
- Alternating Projection-Proximal Methods for Convex Programming and Variational Inequalities
- Using SeDuMi 1.02, A Matlab toolbox for optimization over symmetric cones
- Prox-Method with Rate of Convergence O(1/t) for Variational Inequalities with Lipschitz Continuous Monotone Operators and Smooth Convex-Concave Saddle Point Problems
- Finite-Dimensional Variational Inequalities and Complementarity Problems
- Iteration-Complexity of a Newton Proximal Extragradient Method for Monotone Variational Inequalities and Inclusion Problems
- Rate of Convergence Analysis of Decomposition Methods Based on the Proximal Method of Multipliers for Convex Minimization
- An Inexact Proximal Path-Following Algorithm for Constrained Convex Minimization
- An Inexact Perturbed Path-Following Method for Lagrangian Decomposition in Large-Scale Separable Convex Optimization
- Implicit Functions and Solution Mappings
- Signal Recovery by Proximal Forward-Backward Splitting
- Convex Analysis
- Convex analysis and monotone operator theory in Hilbert spaces
