An Exact Penalty Function Algorithm for Non-smooth Convex Constrained Minimization Problems

This paper presents a readily implementable algorthm for solvingconstrained minimization problems invlving (not necessarilysmooth) convex functions. The algorithm minimizes an exact penaltyfunction via the aggregater subgradient method for unconstrainedminimization, A scheme for automatic limitaion of penalty growthis given. The algorithm is globally convergent under mild assumptions.     

Exact Penalty Functions for Constrained Minimization Problems via Regularized Gap Function for Variational Inequalities

By using the regularized gap function for variational inequalities, we introduce a new penalty function P _α(x) for the problem of minimizing a twice continuously differentiable function in a closed convex subset of the n-dimensional space . Under certain assumptions, it is shown that any stationary point of the penalty function P _α(x) satisfies the first-order optimality condition of the original constrained minimization problem, and any local (or global) minimizer of P _α(x) on is a locally (or globally) optimal solution of the original optimization problem.     

关于约束极小化问题的一个新的简单精确罚函数

针对等式及不等式约束极小化问题,通过对原问题添加一个变量,给出一个新的简单精确罚函数,即在该精确罚函数表达式中,不含有目标函数及约束函数的梯度．在满足某些约束品性的条件下,可以证明:当罚参数充分大时,所给出的罚问题的局部极小点是原问题的局部极小点．     

A Quasi-Newton Penalty Barrier Method for Convex Minimization Problems

We describe an infeasible interior point algorithm for convex minimization problems. The method uses quasi-Newton techniques for approximating the second derivatives and providing superlinear convergence. We propose a new feasibility control of the iterates by introducing shift variables and by penalizing them in the barrier problem. We prove global convergence under standard conditions on the problem data, without any assumption on the behavior of the algorithm.     

Exact Penalty Functions for Convex Bilevel Programming Problems

In this paper, we propose a new constraint qualification for convex bilevel programming problems. Under this constraint qualification, a locally and globally exact penalty function of order 1 for a single-level reformulation of convex bilevel programming problems is given without requiring the linear independence condition and the strict complementarity condition to hold in the lower-level problem. Based on these results, locally and globally exact penalty functions for two other single-level reformulations of convex bilevel programming problems can be obtained. Furthermore, sufficient conditions for partial calmness to hold in some single-level reformulations of convex bilevel programming problems can be given.     

Exact Penalty Functions for Nonlinear Integer Programming Problems

In this work, we study exact continuous reformulations of nonlinear integer programming problems. To this aim, we preliminarily state conditions to guarantee the equivalence between pairs of general nonlinear problems. Then, we prove that optimal solutions of a nonlinear integer programming problem can be obtained by using various exact penalty formulations of the original problem in a continuous space.     

Optimal Control Problems via Exact Penalty Functions

The nonsmoothness is viewed by many people as at least an undesirable (if not unavoidable) property. Our aim here is to show that recent developments in Nonsmooth Analysis (especially in Exact Penalization Theory) allow one to treat successfully even some quite smooth problems by tools of Nonsmooth Analysis and Nondifferentiable Optimization. Our approach is illustrated by one Classical Control Problem of finding optimal parameters in a system described by ordinary differential equations.     

非光滑多目标最优化的一个精确罚函数

本文将文献[1]中的关于单目标规划的精确罚函数的结果推广到局部Lipschitz多目标规划问题上.     

An Infinite-Dimensional Semismooth Newton Method for Elasto-Plastic Contact Problems

A Fenchel dualization scheme for the one-step time-discretized elasto-plastic contact problem with kinematic or isotropic hardening is considered. The associated path is induced by a combined Moreau-Yosida / Tichonov regularization of the dual problem. The sequence of solutions to the regularized problems is shown to converge strongly to the solution of the original problem. This property relies on the density of the intersection of certain convex sets. The corresponding conditions are worked out and customary regularization approaches are shown to be valid in this context. It is also argued that without higher regularity assumptions on the data the resulting problems possess Newton differentiable optimality systems in infinite dimensions [2]. Consequently, each regularized subsystem can be solved mesh-independently at a local superlinear rate of convergence [6]. Numerically the problems are solved using conforming finite elements. (© 2013 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Existence of Exact Penalty for Constrained Optimization Problems in Metric Spaces

In this paper we use the penalty approach in order to study constrained minimization problems in a complete metric space with locally Lipschitzian mixed constraints. A penalty function is said to have the exact penalty property if there is a penalty coefficient for which a solution of an unconstrained penalized problem is a solution of the corresponding constrained problem. In this paper we establish sufficient conditions for the exact penalty property.      

一种新的求解带约束的有限极大极小问题的精确罚函数

提出了一种新的精确光滑罚函数求解带约束的极大极小问题．仅仅添加一个额外的变量,利用这个精确光滑罚函数,将带约束的极大极小问题转化为无约束优化问题． 证明了在合理的假设条件下,当罚参数充分大,罚问题的极小值点就是原问题的极小值点．进一步,研究了局部精确性质．数值结果表明这种罚函数算法是求解带约束有限极大极小问题的一种有效算法．     

On Smoothing l1 Exact Penalty Function for Constrained Optimization Problems

This article introduces a smoothing technique to the l₁ exact penalty function. An application of the technique yields a twice continuously differentiable penalty function and a smoothed penalty problem. Under some mild conditions, the optimal solution to the smoothed penalty problem becomes an approximate optimal solution to the original constrained optimization problem. Based on the smoothed penalty problem, we propose an algorithm to solve the constrained optimization problem. Every limit point of the sequence generated by the algorithm is an optimal solution. Several numerical examples are presented to illustrate the performance of the proposed algorithm.     

二层多目标规划的一个精确罚函数法

本文针对上层为凸的单目标、下层为线性多目标的二层规划问题提出了一个精确罚函数法,讨论了初始罚因子的选取,给出了精确罚因子及其自适应增加机制,并证明了该算法的有限终止性。     

A Lower Bound for the Penalty Parameter in the Exact Minimax Penalty Function Method for Solving Nondifferentiable Extremum Problems

In the paper, we consider the exact minimax penalty function method used for solving a general nondifferentiable extremum problem with both inequality and equality constraints. We analyze the relationship between an optimal solution in the given constrained extremum problem and a minimizer in its associated penalized optimization problem with the exact minimax penalty function under the assumption of convexity of the functions constituting the considered optimization problem (with the exception of those equality constraint functions for which the associated Lagrange multipliers are negative—these functions should be assumed to be concave). The lower bound of the penalty parameter is given such that, for every value of the penalty parameter above the threshold, the equivalence holds between the set of optimal solutions in the given extremum problem and the set of minimizers in its associated penalized optimization problem with the exact minimax penalty function.     

混合整数规划的精确罚函数

本文讨论了混合整数规划的精确罚函数，并给出了原规划的解和其相应的罚问题解的等价性的几个充分条件。此外，我们提出了线性混合整数规划情况下相应的K－K－T条件。     

关于一类等式约束优化的简单光滑精确罚函数

本文给出了一类等式约束优化的简单光滑精确罚函数,该精确罚函数有别于传统罚函数,它是光滑的和简单的,即在该精确罚函数表达式中,不含有目标函数的梯度.     

An Exact Penalty Function Method for Continuous Inequality Constrained Optimal Control Problem

In this paper, we consider a class of optimal control problems subject to equality terminal state constraints and continuous state and control inequality constraints. By using the control parametrization technique and a time scaling transformation, the constrained optimal control problem is approximated by a sequence of optimal parameter selection problems with equality terminal state constraints and continuous state inequality constraints. Each of these constrained optimal parameter selection problems can be regarded as an optimization problem subject to equality constraints and continuous inequality constraints. On this basis, an exact penalty function method is used to devise a computational method to solve these optimization problems with equality constraints and continuous inequality constraints. The main idea is to augment the exact penalty function constructed from the equality constraints and continuous inequality constraints to the objective function, forming a new one. This gives rise to a sequence of unconstrained optimization problems. It is shown that, for sufficiently large penalty parameter value, any local minimizer of the unconstrained optimization problem is a local minimizer of the optimization problem with equality constraints and continuous inequality constraints. The convergent properties of the optimal parameter selection problems with equality constraints and continuous inequality constraints to the original optimal control problem are also discussed. For illustration, three examples are solved showing the effectiveness and applicability of the approach proposed.     

Existence of Exact Penalty and Its Stability for Nonconvex Constrained Optimization Problems in Banach Spaces

In this paper we use the penalty approach in order to study two constrained minimization problems. A penalty function is said to have the generalized exact penalty property if there is a penalty coefficient for which approximate solutions of the unconstrained penalized problem are close enough to approximate solutions of the corresponding constrained problem. In this paper we show that the generalized exact penalty property is stable under perturbations of cost functions, constraint functions and the right-hand side of constraints.     

Simultaneous Decoupling and Disturbance-Rejection Problems for Infinite-Dimensional Systems

Two simultaneous decoupling and disturbance-rejection problemswith state feed-back for infinite dimensional systems are studiedin the framework of a geometric approach. Under certain assumptions,some solvability conditions of these problems are presented.