An algorithm for determining all extreme points of a convex polytope

An algorithm for determining all the extreme points of a convex polytope associated with a set of linear constraints, via the computation of basic feasible solutions to the constraints, is presented. The algorithm is based on the product-form revised simplex method and as such can be readily linked onto standard linear programming codes. Applications of such an algorithm are reviewed and limited computational experience given.     

A pivoting algorithm for convex hulls and vertex enumeration of arrangements and polyhedra

We present a new pivot-based algorithm which can be used with minor modification for the enumeration of the facets of the convex hull of a set of points, or for the enumeration of the vertices of an arrangement or of a convex polyhedron, in arbitrary dimension. The algorithm has the following properties:

An enumerative algorithm for computing all possibly optimal solutions to an interval LP

Interval programming techniques are a valuable approach for tackling uncertainty in mathematical programming models, because they only require the knowledge of the feasible range of variation of the model coefficients. Nevertheless, the use of these techniques has some limitations, namely when the number of decision variables with interval coefficients is high since the number of objective functions at stake in the sub-problem for testing the (weak) efficiency of each non-basic variable may be easily out of an acceptable computational effort. A similar problem may arise with the number of sub-problems for testing the multi-parametric optimality of each solution obtained (that is, to check whether the solution is possibly optimal or not) and the multi-parametric optimality of each edge by using the all emanating edges algorithm. An alternative algorithm is suggested that allows obtaining all possibly optimal solutions, which fulfill the formal criteria of optimality in a feasible bounded region.     

An algorithm for computing all solutions of an absolute value equation

Presented is an algorithm which in a finite (but exponential) number of steps computes all solutions of an absolute value equation Ax + B|x| = b (A, B square), or fails. Failure has never been observed for randomly generated data. The algorithm can also be used for computation of all solutions of a linear complementarity problem.     

A cell exclusion algorithm for determining all the solutions of a nonlinear system of equations

A new group of methods named cell exclusion algorithms (CEAs) is developed for finding all the solutions of a nonlinear system of equations. These types of algorithms, different in principle from those of homotopy, interval and cell-mapping-dynamical-analysis approaches, are based on cellular discretization and the use of a certain simple necessity test of the solutions. The main advantages of the algorithms are their simplicity, reliability, and general applicability. Having all features of interval techniques (but without using interval arithmetic) and with complexity O(log(1/)), the algorithms improve significantly on both the interval algorithms and the cell mapping techniques. Theoretical analysis and numerical simulations both demonstrate that CEAs are very efficient.     

Existence of solutions for nonlinear inequalities in locally convex separated space

An existence result for abstract nonlinear inequalities. As a consequence of our result, we obtain some further generalizations of recent known results. According to this result, we show the existence of g-saddle point and Aumann strong equilibrium for a constrained noncooperative game.     

An algorithm for constrained convex optimization

This paper presents a feasible direction algorithm for the minimization of a pseudoconvex function over a smooth, compact, convex set. We establish that each cluster point of the generated sequence is an optimal solution of the problem without introducing anti-jamming procedures. Each iteration of the algorithm involves as subproblems only one line search for a zero of a continuously differentiable convex function and one univariate function minimization on a compact interval.     

An efficient algorithm for solving inequalities

An efficient algorithm for solving a finite system of inequalities in a finite number of iterations is described and analyzed.This work was supported by the UK Science and Engineering Research Council     

Regularity of solutions for arbitrary order variational inequalities with general convex sets

We consider the regularity of solutions of a system for nonlinear arbitrary order variational inequalities with some general concrete closed convex sets. The maximal function method and the convergence method are used, and a higher integrability of the derivatives is obtained.     

An interior-proximal method for convex linearly constrained problems and its extension to variational inequalities

In this paper, an entropy-like proximal method for the minimization of a convex function subject to positivity constraints is extended to an interior algorithm in two directions. First, to general linearly constrained convex minimization problems and second, to variational inequalities on polyhedra. For linear programming, numerical results are presented and quadratic convergence is established.Corresponding author. His research has been supported by C.E.E grants: CI1* CT 92-0046.     

An efficient algorithm for finding all solutions of separable systems of nonlinear equations

An efficient algorithm is proposed for finding all solutions of systems of nonlinear equations with separable mappings. This algorithm is based on interval analysis, the dual simplex method, the contraction method, and a special technique which makes the algorithm not require large memory space and not require copying tableaus. By numerical examples, it is shown that the proposed algorithm could find all solutions of a system of 2000 nonlinear equations in acceptable computation time. AMS subject classification (2000)  65H10, 65G10     

Further applications of a splitting algorithm to decomposition in variational inequalities and convex programming

A classical method for solving the variational inequality problem is the projection algorithm. We show that existing convergence results for this algorithm follow from one given by Gabay for a splitting algorithm for finding a zero of the sum of two maximal monotone operators. Moreover, we extend the projection algorithm to solveany monotone affine variational inequality problem. When applied to linear complementarity problems, we obtain a matrix splitting algorithm that is simple and, for linear/quadratic programs, massively parallelizable. Unlike existing matrix splitting algorithms, this algorithm converges under no additional assumption on the problem. When applied to generalized linear/quadratic programs, we obtain a decomposition method that, unlike existing decomposition methods, can simultaneously dualize the linear constraints and diagonalize the cost function. This method gives rise to highly parallelizable algorithms for solving a problem of deterministic control in discrete time and for computing the orthogonal projection onto the intersection of convex sets.This research is partially supported by the U.S. Army Research Office, contract DAAL03-86-K-0171 (Center for Intelligent Control Systems), and by the National Science Foundation under grant NSF-ECS-8519058.Thanks are due to Professor J.-S. Pang for his helpful comments.     

An approximate decomposition algorithm for convex minimization

For nonsmooth convex optimization, Robert Mifflin and Claudia Sagastizábal introduce a VU-space decomposition algorithm in Mifflin and Sagastizábal (2005) [11]. An attractive property of this algorithm is that if a primal-dual track exists, this algorithm uses a bundle subroutine. With the inclusion of a simple line search, it is proved to be globally and superlinearly convergent. However, a drawback is that it needs the exact subgradients of the objective function, which is expensive to compute. In this paper an approximate decomposition algorithm based on proximal bundle-type method is introduced that is capable to deal with approximate subgradients. It is shown that the sequence of iterates generated by the resulting algorithm converges to the optimal solutions of the problem. Numerical tests emphasize the theoretical findings.     

An explicit algorithm for monotone variational inequalities

We introduce a fully explicit method for solving monotone variational inequalities in Hilbert spaces, where orthogonal projections onto the feasible set are replaced by projections onto suitable hyperplanes. We prove weak convergence of the whole generated sequence to a solution of the problem, under only the assumptions of continuity and monotonicity of the operator and existence of solutions.