An active set method for solving linearly constrained nonsmooth optimization problems

An algorithm for solving linearly constrained optimization problems is proposed. The search direction is computed by a bundle principle and the constraints are treated through an active set strategy. Difficulties that arise when the objective function is nonsmooth, require a clever choice of a constraint to relax. A certain nondegeneracy assumption is necessary to obtain convergence. Most of this research was performed when the author was with I.N.R.I.A. (Domaine de Voluceau-Rocquencourt, B.P. 105, 78153 Le Chesnay Cédex, France). This research was supported in part by the National Science Foundation, Grants No. DMC-84-51515 and OIR-85-00108.     

An approximate method of solving dynamical problems of thermoviscoelasticity

A generalization of Whitham's asymptotic method is proposed for solving systems of integro-differential equations describing the dynamic behavior of bodies composed of nonlinear viscoelastic materials. The possibility of using this approach to solve coupled problems of thermoviscoelasticity is indicated. A number of examples are considered including the propagation of a harmonic disturbance in a rod of nonlinear viscoelastic material under isothermal conditions and the coupled problem of the high-frequency vibration of a rod of linear viscoelastic material, whose properties depend on temperature in accordance with the time-temperature superposition principle.Institute of Mechanics, Academy of Sciences of the Ukrainian SSR, Kiev. Translated from Mekhanika Polimerov, No. 1, pp. 58–65, January–February, 1973.     

非光滑最优控制问题的一种数值解法

针对非光滑最优控制问题提出一种分段数值解法.首先对问题进行全局拟谱离散,然后选取分点,将时间区域进行剖分,在每段区域上对问题进行离散,离散过程采用Chebyshev-Legendre拟谱方法,可以有效借助快速Legendre变换提高算法的运算效率,比现有算法在很大程度上节省了计算时间.给出了相关的理论分析,数值结果表明方法的高精度和有效性.     

An improved two-step method for solving generalized Nash equilibrium problems

The generalized Nash equilibrium problem (GNEP) is a noncooperative game in which the strategy set of each player, as well as his payoff function, depend on the rival players strategies. As a generalization of the standard Nash equilibrium problem (NEP), the GNEP has recently drawn much attention due to its capability of modeling a number of interesting conflict situations in, for example, an electricity market and an international pollution control. In this paper, we propose an improved two-step (a prediction step and a correction step) method for solving the quasi-variational inequality (QVI) formulation of the GNEP. Per iteration, we first do a projection onto the feasible set defined by the current iterate (prediction) to get a trial point; then, we perform another projection step (correction) to obtain the new iterate. Under certain assumptions, we prove the global convergence of the new algorithm. We also present some numerical results to illustrate the ability of our method, which indicate that our method outperforms the most recent projection-like methods of Zhang et al. (2010).     

Shooting method for solving equilibrium programming problems

A new iterative method is proposed for solving equilibrium programming problems. The sequence of points it generates is proved to converge weakly to the solution set of the equilibrium problem under study. If the initial point has at least one projection onto the solution set of the equilibrium problem, the sequence generated by the method is shown to converge strongly to the set of these projections. The partial gradient of the initial data is assumed to be invertible and strictly monotone, which differs from the classical skew-symmetry condition.     

An extraproximal method for solving equilibrium programming problems in a Hilbert space

A regularized method of the proximal type for solving equilibrium problems in a Hilbert space is proposed. The method is combined with an approximation of the original problem. The convergence of the method is analyzed, and a regularizing operator is constructed.     

A descent method with inexact linear search for nonsmooth equilibrium problems

A descent method with respect to the gap function is formulated and justified for the nonsmooth equilibrium problem. It uses the procedure of inexact linear search of the Armijo type. The proposed method converges under the same assumptions as the methods with exact linear search.     

The interior proximal extragradient method for solving equilibrium problems

In this article we present a new and efficient method for solving equilibrium problems on polyhedra. The method is based on an interior-quadratic proximal term which replaces the usual quadratic proximal term. This leads to an interior proximal type algorithm. Each iteration consists in a prediction step followed by a correction step as in the extragradient method. In a first algorithm each of these steps is obtained by solving an unconstrained minimization problem, while in a second algorithm the correction step is replaced by an Armijo-backtracking linesearch followed by an hyperplane projection step. We prove that our algorithms are convergent under mild assumptions: pseudomonotonicity for the two algorithms and a Lipschitz property for the first one. Finally we present some numerical experiments to illustrate the behavior of the proposed algorithms.     

A nonsmooth Levenberg–Marquardt method for solving semi-infinite programming problems

In this paper, we first transform the semi-infinite programming problem into the KKT system by the techniques in [D.H. Li, L. Qi, J. Tam, S.Y. Wu, A smoothing Newton method for semi-infinite programming, J. Global. Optim. 30 (2004) 169–194; L. Qi, S.Y. Wu, G.L. Zhou, Semismooth Newton methods for solving semi-infinite programming problems, J. Global. Optim. 27 (2003) 215–232]. Then a nonsmooth and inexact Levenberg–Marquardt method is proposed for solving this KKT system based on [H. Dan, N. Yamashita, M. Fukushima, Convergence properties of the inexact Levenberg–Marquardt method under local error bound conditions, Optimim. Methods Softw., 11 (2002) 605–626]. This method is globally and superlinearly (even quadratically) convergent. Finally, some numerical results are given.     

An approximate method for solving the convex programming problem

We consider an iterative process for maximization of a convex nondifferentiable functional in a real Hilbert space. Two-sided bounds on the optimal functional value are derived. Stability of the approximate solutions is considered. Convergence of the proposed iterative process is proved.Translated from Vychislitel'naya i Prikladnaya Matematika, No. 59, pp. 122–129, 1986     

An analytic approximate method for solving stochastic integrodifferential equations

In this paper we compare the solution of a general stochastic integrodifferential equation of the Ito type, with the solutions of a sequence of appropriate equations of the same type, whose coefficients are Taylor series of the coefficients of the original equation. The approximate solutions are defined on a partition of the time-interval. The rate of the closeness between the original and approximate solutions is measured in the sense of the Lp-norm, so that it decreases if the degrees of these Taylor series increase, analogously to real analysis. The convergence with probability one is also proved.     

求解非光滑凸规划的一种混合束方法

提出一种求解非光滑凸规划问题的混合束方法. 该方法通过对目标函数增加迫近项, 且对可行域增加信赖域约束进行迭代, 做为迫近束方法与信赖域束方法的有机结合, 混合束方法自动在二者之间切换, 收敛性分析表明该方法具有全局收敛性. 最后的数值算例验证了算法的有效性．     

A half-space projection method for solving generalized Nash equilibrium problems

The generalized Nash equilibrium problem (GNEP) is an n-person noncooperative game in which each player’s strategy set depends on the rivals’ strategy set. In this paper, we presented a half-space projection method for solving the quasi-variational inequality problem which is a formulation of the GNEP. The difference from the known projection methods is due to the next iterate point in this method is obtained by directly projecting a point onto a half-space. Thus, our next iterate point can be represented explicitly. The global convergence is proved under the minimal assumptions. Compared with the known methods, this method can reduce one projection of a vector onto the strategy set per iteration. Numerical results show that this method not only outperforms the known method but is also less dependent on the initial value than the known method.     

An iterative approximation method for solving a general system of variational inequality problems and mixed equilibrium problems

In this paper, we introduce an iterative algorithm for finding a common element of the set of solutions of a mixed equilibrium problem, the set of fixed points of an infinite family of nonexpansive mappings and the set of solutions of a general system of variational inequalities for a cocoercive mapping in a real Hilbert space. Furthermore, we prove that the proposed iterative algorithm converges strongly to a common element of the above three sets. Our results extend and improve the corresponding results of Ceng, Wang, and Yao [L.C. Ceng, C.Y. Wang, J.C. Yao, Strong convergence theorems by a relaxed extragradient method for a general system of variational inequalities, Math. Methods Oper. Res. 67 (2008) 375–390], Ceng and Yao [L.C. Ceng, J.C. Yao, A hybrid iterative scheme for mixed equilibrium problems and fixed point problems, J. Comput. Appl. Math. doi:10.1016/j.cam.2007.02.022], Takahashi and Takahashi [S. Takahashi, W. Takahashi, Viscosity approximation methods for equilibrium problems and fixed point problems in Hilbert spaces, J. Math. Anal. Appl. 331 (2007) 506–515] and many others.     

The CoMirror algorithm for solving nonsmooth constrained convex problems

We introduce a first-order Mirror-Descent (MD) type algorithm for solving nondifferentiable convex problems having a combination of simple constraint set X (ball, simplex, etc.) and an additional functional constraint. The method is tuned to exploit the structure of X by employing an appropriate non-Euclidean distance-like function. Convergence results and efficiency estimates are derived. The performance of the algorithm is demonstrated by solving certain image deblurring problems.     

Second-order necessary efficiency conditions for nonsmooth vector equilibrium problems

This paper presents primal and dual second-order Fritz John necessary conditions for weak efficiency of nonsmooth vector equilibrium problems involving inequality, equality and set constraints in terms of the Páles–Zeidan second-order directional derivatives. Dual second-order Karush–Kuhn–Tucker necessary conditions for weak efficiency are established under suitable second-order constraint qualifications.