Journal of Global Optimization
Data Driven Similarity Measures for k-Means Like Clustering Algorithms
Information Retrieval
Numerical Comparison of Augmented Lagrangian Algorithms for Nonconvex Problems
Computational Optimization and Applications
Double-Regularization Proximal Methods, with Complementarity Applications
Computational Optimization and Applications
Nonlinear Rescaling as Interior Quadratic Prox Method in Convex Optimization
Computational Optimization and Applications
Modified proximal-point method for nonlinear complementarity problems
Journal of Computational and Applied Mathematics
Computers & Mathematics with Applications
Nonmonotone projected gradient methods based on barrier and Euclidean distances
Computational Optimization and Applications
Dual convergence of the proximal point method with Bregman distances for linear programming
Optimization Methods & Software
A generalized proximal-point-based prediction-correction method for variational inequality problems
Journal of Computational and Applied Mathematics
A new predicto-corrector method for pseudomonotone nonlinear complementarity problems
International Journal of Computer Mathematics
Primal-dual exterior point method for convex optimization
Optimization Methods & Software
The interior proximal extragradient method for solving equilibrium problems
Journal of Global Optimization
Inexact Proximal Point Methods for Variational Inequality Problems
SIAM Journal on Optimization
A self-adaptive gradient projection algorithm for the nonadditive traffic equilibrium problem
Computers and Operations Research
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We study a class of interior proximal algorithms and nonquadratic multiplier methods for solving convex programs, where the usual proximal quadratic term is replaced by an homogeneous functional of order two, defined in terms of a convex function. We prove, under mild assumptions, several new convergence results in both the primal and dual framework allowing also for approximate minimization. In particular, we introduce a new class of interior proximal methods which is globally convergent and allows for generating Cinfinity multiplier methods with bounded Hessians which exhibit strong convergence properties. We also consider linearly constrained convex problems and establish global quadratic convergence rates results for linear programs. We then study in detail a particular realization of these algorithms, leading to a new class of logarithmic-quadratic interior point algorithms which are shown to enjoy several attractive properties for solving constrained convex optimization problems.