The Glowinski–Le Tallec splitting method revisited in the framework of equilibrium problems in Hilbert spaces

In this paper, we introduce a new approach for solving equilibrium problems in Hilbert spaces. First, we transform the equilibrium problem into the problem of finding a zero of a sum of two maximal monotone operators. Then, we solve the resulting problem using the Glowinski–Le Tallec splitting method and we obtain a linear rate of convergence depending on two parameters. In particular, we enlarge significantly the range of these parameters given rise to the convergence. We prove that the sequence generated by the new method converges to a global solution of the considered equilibrium problem. Finally, numerical tests are displayed to show the efficiency of the new approach.     

A bracketing technique to ensure desirable convergence in univariate minimization

This paper gives a general safeguarded bracketing technique for minimizing a function of a single variable. In certain cases the technique guarantees convergence to a stationary point and, when combined with sequential polynomial and/or polyhedral fitting algorithms, preserves rapid convergence. Each bracket has an interior point whose function value does not exceed those of the two bracket endpoints. The safeguarding technique consists of replacing the fitting algorithm's iterate candidate by a close point whose distance from the three bracket points exceeds a positive multiple of the square of the bracket length. It is shown that a given safeguarded quadratic fitting algorithm converges in a certain better than linear manner with respect to the bracket endpoints for a strongly convex twice continuously differentiable function.Research sponsored by the Institut National de Recherche en Informatique et en Automatique, Rocquencourt, France, and by the Air Force Office of Scientific Research, Air Force System Command, USAF, under Grant Number AFOSR-83-0210. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon.Research sponsored, in part, by the Institut National de Recherche en Informatique et en Automatique, Rocquencourt, France.     

ε-Optimal solutions in nondifferentiable convex programming and some related questions

In this paper we present ε-optimality conditions of the Kuhn-Tucker type for points which are within ε of being optimal to the problem of minimizing a nondifferentiable convex objective function subject to nondifferentiable convex inequality constraints, linear equality constraints and abstract constraints. Such ε-optimality conditions are of interest for theoretical consideration as well as from the computational point of view. Some illustrative applications are made. Thus we derive an expression for the ε-subdifferential of a general convex ‘max function’. We also show how the ε-optimality conditions given in this paper can be mechanized into a bundle algorithm for solving nondifferentiable convex programming problems with linear inequality constraints.     

Some properties of a uniformly linearly independent sequence of subspaces

The concept of a uniformly linearly independent sequence, due to R.M. Elkin, is a useful notion. Convergence theory of iterative processes for solving nonlinear equations or optimization problems in $\text{R}$ⁿ is an example of a discipline which has benefited from the use of this notion. The purpose of this paper is to present some properties of a uniformly linearly independent sequence of subspaces of $\text{R}$ⁿ. The properties derived were motivated by convergence results of Elkin for “block univariate relaxation” methods.     

On the convergence rate for a penalty function method of exponential type

Recently, Kort and Bertsekas (Ref. 1) and Hartman (Ref. 2) presented independently a new penalty function algorithm of exponential type for solving inequality-constrained minimization problems. The main purpose of this work is to give a proof on the rate of convergence of a modification of the exponential penalty method proposed by these authors. We show that the sequence of points generated by the modified algorithm converges to the solution of the original nonconvex problem linearly and that the sequence of estimates of the optimal Lagrange multiplier converges to this multiplier superlinearly. The question of convergence of the modified method is discussed. The present paper hinges on ideas of Mangasarian (Ref. 3), but the case considered here is not covered by Mangasarian's theory.     

Extragradient Methods and Linesearch Algorithms for Solving Ky Fan Inequalities and Fixed Point Problems

In this paper, we introduce some new iterative methods for finding a common element of the set of points satisfying a Ky Fan inequality, and the set of fixed points of a contraction mapping in a Hilbert space. The strong convergence of the iterates generated by each method is obtained thanks to a hybrid projection method, under the assumptions that the fixed-point mapping is a ??-strict pseudocontraction, and the function associated with the Ky Fan inequality is pseudomonotone and weakly continuous. A?Lipschitz-type condition is assumed to hold on this function when the basic iteration comes from the extragradient method. This assumption is unnecessary when an Armijo backtracking linesearch is incorporated in the extragradient method. The particular case of variational inequality problems is examined in a last section.     

TheC M -embedded problem and optimality conditions

In this paper theC _M -embedded problem which is also called the design centering problem in other papers will be described, and new optimality conditions and some results associated with optimality conditions will be presented. These results hold for general non-convex regions. To a certain extent they provide the possibility to develop search techniques. It should be pointed out that, in this paper, the only case where the Minkowski norm is just the Euclidean norm is treated.     

Generalized Hessian matrix and second-order optimality conditions for problems withC 1,1 data

In this paper, we present a generalization of the Hessian matrix toC ^1,1 functions, i.e., to functions whose gradient mapping is locally Lipschitz. This type of function arises quite naturally in nonlinear analysis and optimization. First the properties of the generalized Hessian matrix are investigated and then some calculus rules are given. In particular, a second-order Taylor expansion of aC ^1,1 function is derived. This allows us to get second-order optimality conditions for nonlinearly constrained mathematical programming problems withC ^1,1 data.     

An exponential penalty method for nondifferentiable minimax problems with general constraints

A well-known approach to constrained minimization is via a sequence of unconstrained optimization computations applied to a penalty function. This paper shows how it is possible to generalize Murphy's penalty method for differentiable problems of mathematical programming (Ref. 1) to solve nondifferentiable problems of finding saddle points with constraints. As in mathematical programming, it is shown that the method has the advantages of both Fiacco and McCormick exterior and interior penalty methods (Ref. 2). Under mild assumptions, the method has the desirable property that all trial solutions become feasible after a finite number of iterations. The rate of convergence is also presented. It should be noted that the results presented here have been obtained without making any use of differentiability assumptions.