A method of centers algorithm for certain minimax problems

An algorithm is presented for the numerical solution of nonlinear programming problems in which the objective function is to be minimized over feasiblex after having been maximized over feasibley. The vectorsx andy are subjected to separate nonlinear constraints. The algorithm is obtained as follows: One starts with an outer algorithm for the minimization overx, that algorithm being taken here to be a method of centers; then, one inserts into this algorithm an adaptive inner procedure, which is designed to compute a suitable approximation to the maximizery in a finite number of steps. The outer and inner algorithms are blended in such a way as to cause the inner one to converge more rapidly. The results on convergence and rate of convergence for the outer algorithm continue to hold (essentially) for the composite algorithm. Thus, what is considered here, for the first time for this type of problem, is the question of how one inserts an approximation procedure into an algorithm so as to preserve its convergence and its rate of convergence.     

Test problems in stochastic multistage programming

This paper provides a set of stochastic multistage programs where the evolvement of uncertain factors is given by stochastic processes. We treat a practical problem statement within the field of managing fixed-income securities. Detailed information on the used parameter values in various interest rate models is given. Barycentric approximation is applied to obtain computational results; different measures of the achieved goodness of approximation are indicated     

Some iterative methods for finding fixed points and for solving constrained convex minimization problems

The present paper is divided into two parts. In the first part, we introduce implicit and explicit iterative schemes for finding the fixed point of a nonexpansive mapping defined on the closed convex subset of a real Hilbert space. We establish results on the strong convergence of the sequences generated by the proposed schemes to a fixed point of a nonexpansive mapping. Such a fixed point is also a solution of a variational inequality defined on the set of fixed points. In the second part, we propose implicit and explicit iterative schemes for finding the approximate minimizer of a constrained convex minimization problem and prove that the sequences generated by our schemes converge strongly to a solution of the constrained convex minimization problem. Such a solution is also a solution of a variational inequality defined over the set of fixed points of a nonexpansive mapping. The results of this paper extend and improve several results presented in the literature in the recent past.     

A projection-filter method for solving nonlinear complementarity problems

The Josephy-Newton method attacks nonlinear complementarity problems which consists of solving, possibly inexactly, a sequence of linear complementarity problems. Under appropriate regularity assumptions, this method is known to be locally (superlinearly) convergent. Utilizing the filter method, we presented a new globalization strategy for this Newton method applied to nonlinear complementarity problem without any merit function. The strategy is based on the projection-proximal point and filter methodology. Our linesearch procedure uses the regularized Newton direction to force global convergence by means of a projection step which reduces the distance to the solution of the problem. The resulting algorithm is globally convergent to a solution. Under natural assumptions, locally superlinear rate of convergence was established.     

On convergence of a new secant-like method for solving nonlinear equations

In this paper, we prove that the order of a new secant-like method presented recently with the so-called order of 2.618 is only 2.414. Some mistakes in the derivation leading to such a conclusion are pointed out. Meanwhile, under the assumption that the second derivative of the involved function is bounded, the convergence radius of the secant-like method is given, and error estimates matching its convergence order are also provided by using a generalized Fibonacci sequence.     

A subgradient algorithm for certain minimax and minisum problems

We present a subgradient algorithm for minimizing the maximum of a finite collection of functions. It is assumed that each function is the sum of a finite collection of basic convex functions and that the number of different subgradient sets associated with nondifferentiable points of each basic function is finite on any bounded set. Problems belonging to this class include the linear approximation problem and both the minimax and minisum problems of location theory. Convergence of the algorithm to an epsilon-optimal solution is proven and its effectiveness is demonstrated by solving a number of location problems and linear approximation problems.This research was partially supported by the Army Research Office, Triangle Park, NC, under contract number DAH-CO4-75-G-0150, and by NSF grants ENG 16-24294 and ENG 75-10225.     

A stable method for solving certain constrained least squares problems

This paper presents a feasible descent algorithm for solving certain constrained least squares problems. These problems are specially structured quadratic programming problems with positive semidefinite Hessian matrices that are allowed to be singular. The algorithm generates a finite sequence of subproblems that are solved using the numerically stable technique of orthogonal factorization with reorthogonalization and Given's transformation updating.This material is based upon work supported by the National Science Foundation under Grant No. MCS 78-06716 and by the International Institute for Applied Systems Analysis.     

A method of local convex majorants for solving variational-like inequalities

A numerical method based on convex approximations that locally majorize a gap function is proposed for solving a variational-like inequality. The algorithm is theoretically validated and the results of comparison of its numerical efficiency to that of the conventional methods are presented.     

A quadratically convergent polynomial long-step algorithm for A class of nonlinear monotone complementarity problems *

Several interior point algorithms have been proposed for solving nonlinear monotone complementarity problems. Some of them have polynomial worst-case complexity but have to confine to short steps, whereas some of the others can take long steps but no polynomial complexity is proven. This paper presents an algorithm which is both long-step and polynomial. In addition, the sequence generated by the algorithm, as well as the corresponding complementarity gap, converges quadratically. The proof of the polynomial complexity requires that the monotone mapping satisfies a scaled Lipschitz condition, while the quadratic rate of convergence is derived under the assumptions that the problem has a strictly complementary solution and that the Jacobian of the mapping satisfies certain regularity conditions     

Iterative processes for solving incorrect convex variational problems

The present paper is concerned with a general approach to the construction and the numerical analysis of stable methods solving semi-infinite convex programs and variational inequalities of elliptical type in case where the considered problems are incorrect. The approach which is based on the application of the PROX-regularization (cf. Martinet, 1970; Ekeland and Temam, 1976; Rockafellar, 1976; Brézis and Lions, 1978; Lemaire, 1988) secures the strong convergence of the minimizing sequence. The possibility of the algorithmical realization is described and depends on the smoothness properties of the solutions.Supported by Deutsche Forschungsgemeinschaft under grant Ti 191/1-1.     

A general regularized gradient-projection method for solving equilibrium and constrained convex minimization problems

In this article, we provide a general iterative method for solving an equilibrium and a constrained convex minimization problem. By using the idea of regularized gradient-projection algorithm (RGPA), we find a common element, which is also a solution of a variational inequality problem. Then the strong convergence theorems are obtained under suitable conditions.     

An iterative scheme for variational inequalities

In this paper we introduce and study a general iterative scheme for the numerical solution of finite dimensional variational inequalities. This iterative scheme not only contains, as special cases the projection, linear approximation and relaxation methods but also induces new algorithms. Then, we show that under appropriate assumptions the proposed iterative scheme converges by establishing contraction estimates involving a sequence of norms in Eⁿ induced by symmetric positive definite matrices G_m. Thus, in contrast to the above mentioned methods, this technique allows the possibility of adjusting the norm at each step of the algorithm. This flexibility will generally yield convergence under weaker assumptions.     

Modified block iterative method for solving convex feasibility problem,equilibrium problems and variational inequality problems

The purpose of this paper is by using the modified block iterative method to propose an algorithm for finding a common element in the intersection of the set of common fixed points of an infinite family of quasi-φ-asymptotically nonexpansive and the set of solutions to an equilibrium problem and the set of solutions to a variational inequality. Under suitable conditions some strong convergence theorems are established in 2-uniformly convex and uniformly smooth Banach spaces. As applications we utilize the results presented in the paper to solving the convex feasibility problem (CFP) and zero point problem of maximal monotone mappings in Banach spaces. The results presented in the paper improve and extend the corresponding results announced by many authors.     

On penalty methods for minimax problems

Penalty algorithms for constrained minimax problems typically involve a sequence of unconstrained approximates which is pointwise monotone in each variable. This paper generalizes convergence results for a wider class of algorithms while imposing conditions which are close to being minimal.Sponsored in part by a grant from the Norwegian Council of Scientific and Technological Research.     

A finite steps algorithm for solving convex feasibility problems

This paper develops a new variant of the classical alternating projection method for solving convex feasibility problems where the constraints are given by the intersection of two convex cones in a Hilbert space. An extension to the feasibility problem for the intersection of two convex sets is presented as well. It is shown that one can solve such problems in a finite number of steps and an explicit upper bound for the required number of steps is obtained. As an application, we propose a new finite steps algorithm for linear programming with linear matrix inequality constraints. This solution is computed by solving a sequence of a matrix eigenvalue decompositions. Moreover, the proposed procedure takes advantage of the structure of the problem. In particular, it is well adapted for problems with several small size constraints.     

Solution techniques for some allocation problems

This paper presents methods for solving allocation problems that can be stated as convex knapsack problems with generalized upper bounds. Such bounds may express upper limits on the total amount allocated to each of several subsets of activities. In addition our model arises as a subproblem in more complex mathematical programs. We therefore emphasize efficient procedures to recover optimality when minor changes in the parameters occur from one problem instance to the next. These considerations lead us to propose novel data structures for such problems. Also, we introduce an approximation method to solve certain equations, which arise during the procedures.     

Dual support method for solving convex quadratic programs

In this article, we present a new dual method for solving convex (but not strictly convex) quadratic programs (QPs). Our method is the generalization of the dual support method, developed by Gabasov and co-workers in 1981, for solving convex QPs. It proceeds in two phases: the first is to construct the initial support, called coordinator support, for the problem and the second is to achieve the optimality of the problem. Results of numerical experiments are given comparing our approach with the active-set method.     

A SUCCESSIVE APPROXIMATION METHOD FOR SOLVING PROBABILISTIC CONSTRAINED PROGRAMS

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Gradient-type penalty method with inertial effects for solving constrained convex optimization problems with smooth data

We consider the problem of minimizing a smooth convex objective function subject to the set of minima of another differentiable convex function. In order to solve this problem, we propose an algorithm which combines the gradient method with a penalization technique. Moreover, we insert in our algorithm an inertial term, which is able to take advantage of the history of the iterates. We show weak convergence of the generated sequence of iterates to an optimal solution of the optimization problem, provided a condition expressed via the Fenchel conjugate of the constraint function is fulfilled. We also prove convergence for the objective function values to the optimal objective value. The convergence analysis carried out in this paper relies on the celebrated Opial Lemma and generalized Fejér monotonicity techniques. We illustrate the functionality of the method via a numerical experiment addressing image classification via support vector machines.