On nonconvex optimization problems with separated nonconvex variables

A mathematical programming problem is said to have separated nonconvex variables when the variables can be divided into two groups: x=(x ₁,...,x _n ) and y=( y ₁,...,y _n ), such that the objective function and any constraint function is a sum of a convex function of (x, y) jointly and a nonconvex function of x alone. A method is proposed for solving a class of such problems which includes Lipschitz optimization, reverse convex programming problems and also more general nonconvex optimization problems.     

On constrained optimization with nonconvex regularization

Numerical Algorithms - In many engineering applications, it is necessary to minimize smooth functions plus penalty (or regularization) terms that violate smoothness and convexity. Specific...     

On nonconvex optimization with integral constraints

We consider the problem of minimizing f(y)dm with y dm=c,c fixed. The functionf is assumed to be continuous, but need not be convex. For this problem, we give necessary and sufficient conditions for the existence of solutions. We also give conditions under which uniqueness in a certain sense holds, and we show a relation which holds between the minimizers of two different problems and the corresponding values of the constraintsc.This research was supported by FINEP-Brazil, Grant Nos. 62.24-0416-00 and 4.2.82.0719-00.     

Vector optimization problems with nonconvex preferences

In this paper, some vector optimization problems are considered where pseudo-ordering relations are determined by nonconvex cones in Banach spaces. We give some characterizations of solution sets for vector complementarity problems and vector variational inequalities. When the nonconvex cone is the union of some convex cones, it is shown that the solution set of these problems is either an intersection or an union of the solution sets of all subproblems corresponding to each of these convex cones depending on whether these problems are defined by the nonconvex cone itself or its complement. Moreover, some relations of vector complementarity problems, vector variational inequalities, and minimal element problems are also given. While this paper was being revised in September 2006, Professor Alex Rubinov (the second author of the paper) left us due to the illness. This is a very sad news to us. We dedicate this paper to the memory of Professor Rubinov as a mathematician and truly friend.     

Local search in problems with nonconvex constraints

Nonconvex optimization problems with an inequality constraint given by the difference of two convex functions (by a d.c. function) are considered. Two methods for finding local solutions to this problem are proposed that combine the solution of partially linearized problems and descent to a level surface of the d.c. function. The convergence of the methods is analyzed, and stopping criterions are proposed. The methods are compared by testing them in a numerical experiment.     

Vector equilibrium flows with nonconvex ordering relations

In this note we introduce the concept of vector network equilibrium flows when the ordering cone is the union of finitely many closed and convex cones. We show that the set of vector network equilibrium flows is equal to the intersection of finitely many sets, where each set is a collection of vector equilibrium flows with respect to a closed and convex cone. Sufficient and necessary conditions for a vector equilibrium flow are presented in terms of scalar equilibrium flows.     

A geometric characterization of strong duality in nonconvex quadratic programming with linear and nonconvex quadratic constraints

We first establish a relaxed version of Dines theorem associated to quadratic minimization problems with finitely many linear equality and a single (nonconvex) quadratic inequality constraints. The case of unbounded optimal valued is also discussed. Then, we characterize geometrically the strong duality, and some relationships with the conditions employed in Finsler theorem are established. Furthermore, necessary and sufficient optimality conditions with or without the Slater assumption are derived. Our results can be used to situations where none of the results appearing elsewhere are applicable. In addition, a revisited theorem due to Frank and Wolfe along with that due to Eaves is established for asymptotically linear sets.     

p-Adic nonconvex compactoids

A bounded subset X of a Banach space over a non-archimedean field ϰ is a compactoid if and only if each basic sequence in X tends to zero (Theorem 2). As a consequence the notions “weakly precompact’ and ‘precompact’ are identical for members of a wide class of ϰ-Banach spaces (Theorem 3).     

Low-rank factorization for rank minimization with nonconvex regularizers

Computational Optimization and Applications - Rank minimization is of interest in machine learning applications such as recommender systems and robust principal component analysis. Minimizing the...     

Global optimization of nonconvex problems with convex-transformable intermediates

This paper addresses the global optimization of problems which contain convex-transformable functions. We present algorithms for identification of convex-transformable functions in general nonconvex problems, and introduce a new class of cutting planes based on recently developed relaxations for convex-transformable functions. These cutting planes correspond to the supporting hyperplanes of these convex relaxations. We integrate our recognition and cutting plane generation algorithms into the global solver BARON, and test our implementation by conducting numerical experiments on a large collection of nonconvex problems. Results demonstrate that the proposed implementation accelerates the convergence speed of the branch-and-bound algorithm, by significantly reducing computational time, number of nodes in the search tree, and required memory.     

Duality of nonconvex optimization with positively homogeneous functions

We consider an optimization problem with positively homogeneous functions in its objective and constraint functions. Examples of such positively homogeneous functions include the absolute value function and the p-norm function, where p is a positive real number. The problem, which is not necessarily convex, extends the absolute value optimization proposed in Mangasarian (Comput Optim Appl 36:43–53, 2007). In this work, we propose a dual formulation that, differently from the Lagrangian dual approach, has a closed-form and some interesting properties. In particular, we discuss the relation between the Lagrangian duality and the one proposed here, and give some sufficient conditions under which these dual problems coincide. Finally, we show that some well-known problems, e.g., sum of norms optimization and the group Lasso-type optimization problems, can be reformulated as positively homogeneous optimization problems.     

A maxmin location problem with nonconvex feasible region

The problem dealt with consists of locating a point in a given convex polyhedron which maximizes the minimum Euclidean distance from a given set of convex polyhedra representing protected areas around population points. The paper describes a finite dominating solution set for the optimal solution and develops a geometrical procedure for obtaining the optimal solution comparing a finite number of candidates.     

DC decomposition of nonconvex polynomials with algebraic techniques

We consider the problem of decomposing a multivariate polynomial as the difference of two convex polynomials. We introduce algebraic techniques which reduce this task to linear, second order cone, and semidefinite programming. This allows us to optimize over subsets of valid difference of convex decompositions (dcds) and find ones that speed up the convex–concave procedure. We prove, however, that optimizing over the entire set of dcds is NP-hard.     

On nonconvex equilibrium problems

In this paper, we introduce a new class of equilibrium problems, known as mixed quasi nonconvex equilibrium problems. We suggest some iterative schemes for solving nonconvex equilibrium problems by using the auxiliary principle technique. The convergence of the proposed methods either requires partially relaxed strongly monotonicity or pseudomonotonicity. As special cases, we obtain a number of known and new results for solving various classes of equilibrium and variational inequality problems.     

Triangulating a nonconvex polytope

This paper is concerned with the problem of partitioning a three-dimensional nonconvex polytope into a small number of elementary convex parts. The need for such decompositions arises in tool design, computer-aided manufacturing, finite-element methods, and robotics. Our main result is an algorithm for decomposing a nonconvex polytope of zero genus withn vertices andr reflex edges intoO(n +r ²) tetrahedra. This bound is asymptotically tight in the worst case. The algorithm requiresO(n +r ²) space and runs inO((n +r ²) logr) time.This research was supported in part by the National Science Foundation under Grant CCR-8700917.     

Relaxation in nonconvex optimal control problems with subdifferential operators

We consider the minimization problem of an integral functional in a separable Hilbert space with integrand not convex in the control defined on solutions of the control system described by nonlinear evolutionary equations with mixed nonconvex constraints. The evolutionary operator of the system is the subdifferential of a proper, convex, lower semicontinuous function depending on time. Along with the initial problem, the author considers the relaxed problem with the convexicated control constraint and the integrand convexicated with respect to the control. Under sufficiently general assumptions, it is proved that the relaxed problem has an optimal solution, and for any optimal solution, there exists a minimizing sequence of the initial problem converging to the optimal solution with respect to trajectories and the functional. An example of a controlled parabolic variational inequality with obstacle is considered in detail. __________ Translated from Sovremennaya Matematika i Ee Prilozheniya (Contemporary Mathematics and Its Applications), Vol. 26, Nonlinear Dynamics, 2005.