Solving nearly-separable quadratic optimization problems as nonsmooth equations
DOI10.1007/s10589-017-9895-8zbMath1370.90169OpenAlexW2583600481MaRDI QIDQ2013144
Arvind U. Raghunathan, Frank E. Curtis
Publication date: 3 August 2017
Published in: Computational Optimization and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10589-017-9895-8
complementarity problemsdual decompositionsemismooth Newton methodsFischer-Burmeister functionquadratic optimization problems
Numerical mathematical programming methods (65K05) Numerical methods involving duality (49M29) Numerical optimization and variational techniques (65K10) Quadratic programming (90C20) Numerical methods based on necessary conditions (49M05) Newton-type methods (49M15) Complementarity and equilibrium problems and variational inequalities (finite dimensions) (aspects of mathematical programming) (90C33) Decomposition methods (49M27)
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Smooth minimization of non-smooth functions
- Combining Lagrangian decomposition and excessive gap smoothing technique for solving large-scale separable convex optimization problems
- On the optimal design of water distribution networks: a practical MINLP approach
- A parallel quadratic programming method for dynamic optimization problems
- Interior-point Lagrangian decomposition method for separable convex optimization
- A semismooth equation approach to the solution of nonlinear complementarity problems
- Introductory lectures on convex optimization. A basic course.
- A theoretical and numerical comparison of some semismooth algorithms for complementarity problems
- Parallel multi-block ADMM with \(o(1/k)\) convergence
- MINQ8: general definite and bound constrained indefinite quadratic programming
- A globally convergent primal-dual interior-point filter method for nonlinear programming
- Directional derivatives of the solution of a parametric nonlinear program
- Path-following gradient-based decomposition algorithms for separable convex optimization
- A nonsmooth version of Newton's method
- Parallel interior-point solver for structured quadratic programs: Application to financial planning problems
- A line search filter approach for the system of nonlinear equations
- On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
- Iteration complexity analysis of dual first-order methods for conic convex programming
- The Semismooth Algorithm for Large Scale Complementarity Problems
- A New Merit Function For Nonlinear Complementarity Problems And A Related Algorithm
- A New Nonsmooth Equations Approach to Nonlinear Complementarity Problems
- The Linear Complementarity Problem
- Monotone Operators and the Proximal Point Algorithm
- Sensitivity analysis for nonlinear programming using penalty methods
- Semismooth and Semiconvex Functions in Constrained Optimization
- Regularity Properties of a Semismooth Reformulation of Variational Inequalities
- A special newton-type optimization method
- Introduction to Stochastic Programming
- A Multidimensional Filter Algorithm for Nonlinear Equations and Nonlinear Least-Squares
- On the Global Convergence of a Filter--SQP Algorithm
- Global Convergence of a Trust-Region SQP-Filter Algorithm for General Nonlinear Programming
- Application of a Smoothing Technique to Decomposition in Convex Optimization
- An Inexact Perturbed Path-Following Method for Lagrangian Decomposition in Large-Scale Separable Convex Optimization
- Global Optimization of Nonlinear Network Design
- Excessive Gap Technique in Nonsmooth Convex Minimization
- Projected filter trust region methods for a semismooth least squares formulation of mixed complementarity problems
- Nonlinear programming without a penalty function.
This page was built for publication: Solving nearly-separable quadratic optimization problems as nonsmooth equations