Knapsack feasibility as an absolute value equation solvable by successive linear programming

We formulate the NP-hard n-dimensional knapsack feasibility problem as an equivalent absolute value equation (AVE) in an n-dimensional noninteger real variable space and propose a finite succession of linear programs for solving the AVE. Exact solutions are obtained for 1,880 out of 2,000 randomly generated consecutive knapsack feasibility problems with dimensions between 500 and one million. For the 120 approximately solved problems the error consists of exactly one noninteger component with value in (0, 1), which when replaced by 0, results in a relative error of less than 0.04%. We also give a necessary and sufficient condition for the solvability of the knapsack feasibility problem in terms of minimizing a concave quadratic function on a polyhedral set. Average time for solving exactly a million-variable knapsack feasibility problem was less than 14 s on a 4 GB machine.     

Absolute value equation solution via concave minimization

The NP-hard absolute value equation (AVE) Ax − |x| = b where and is solved by a succession of linear programs. The linear programs arise from a reformulation of the AVE as the minimization of a piecewise-linear concave function on a polyhedral set and solving the latter by successive linearization. A simple MATLAB implementation of the successive linearization algorithm solved 100 consecutively generated 1,000-dimensional random instances of the AVE with only five violated equations out of a total of 100,000 equations.     

Infinite programming and theorems of the alternative

In this paper, we obtain optimal versions of the Karush–Kuhn–Tucker, Lagrange multiplier, and Fritz John theorems for a nonlinear infinite programming problem where both the number of equality and inequality constraints is arbitrary. To this end, we make use of a theorem of the alternative for a family of functions satisfying a certain type of weak convexity, the so‐called infsup‐convexity.     

Nondifferentiable mathematical programming and convex-concave functions

For a convex-concave functionL(x, y), we define the functionf(x) which is obtained by maximizingL with respect toy over a specified set. The minimization problem with objective functionf is considered. We derive necessary conditions of optimality for this problem. Based upon these necessary conditions, we define its dual problem. Furthermore, a duality theorem and a converse duality theorem are obtained. It is made clear that these results are extensions of those derived in studies on a class of nondifferentiable mathematical programming problems.This work was supported by the Japan Society for the Promotion of Sciences.     

On various duality theorems in nonlinear programming

Recently, Gulati and Craven and Mond and Egudo established strict converse duality theorems for some of Mond-Weir duals for nonlinear programming problems. Here, we establish various duality theorems under weaker convexity conditions that are different from those of Gulati and Craven, Mond and Weir, and Mond and Egudo.The first author is thankful to the Natural Science and Engineering Research Council of Canada for financial support through Grant A-5319.     

An exact duality theory for semidefinite programming and its complexity implications

In this paper, an exact dual is derived for Semidefinite Programming (SDP), for which strong duality properties hold without any regularity assumptions. Its main features are: (i) The new dual is an explicit semidefinite program with polynomially many variables and polynomial size coefficient bitlengths. (ii) If the primal is feasible, then it is bounded if and only if the dual is feasible. (iii) When the primal is feasible and bounded, then its optimum value equals that of the dual, or in other words, there is no duality gap. Further, the dual attains this common optimum value. (iv) It yields a precise theorem of the alternative for semidefinite inequality systems, i.e. a characterization of theinfeasibility of a semidefinite inequality in terms of thefeasibility of another polynomial size semidefinite inequality. The standard duality for linear programming satisfies all of the above features, but no such explicit gap-free dual program of polynomial size was previously known for SDP, without Slater-like conditions being assumed. The dual is then applied to derive certain complexity results for SDP. The decision problem of Semidefinite Feasibility (SDFP), which asks to determine if a given semidefinite inequality system is feasible, is the central problem of interest, he complexity of SDFP is unknown, but we show the following: (i) In the Turing machine model, the membership or nonmembership of SDFP in NP and Co-NP is simultaneous; hence SDFP is not NP-Complete unless NP=Co-NP. (ii) In the real number model of Blum, Shub and Smale, SDFP is in NP∩CoNP.     

Absolute Valued Strongly Power-Associative Triple Systems

Absolute valued triple system are the natural ternary extension of absolute valued algebras. In this article we classify strongly power-associative absolute valued triple systems.     

Theorems of the Alternative and Optimality Conditions for Convexlike and General Convexlike Programming

A general alternative theorem for convexlike functions is given. This permits the establishment of optimality conditions for convexlike programming problems in which both inequality and equality constraints are considered. It is shown that the main results of the paper contain, in particular, those of Craven, Giannessi, Jeyakumar, Hayashi, and Komiya, Simons, Zlinescu, and a recent result of Tamminen.     

An extension lemma and homogeneous programming

An extension lemma, which is equivalent to the generalized Gordan's theorem of the alternative, due to Fan, Glicksberg, and Hoffman, is applied to present a duality theory for a general class of homogeneous programs, with and without a constraint qualification of Slater type. In addition, an existence theorem for optimal solutions of homogeneous programs is given.The author thanks an anonymous referee for valuable suggestions about an earlier draft of this paper.     

Optimal value function in semi-infinite programming

In this paper, we provide a systematic approach to the main topics in linear semi-infinite programming by means of a new methodology based on the many properties of the sub-differential mapping and the closure of a given convex function. In particular, we deal with the duality gap problem and its relation to the discrete approximation of the semi-infinite program. Moreover, we have made precise the conditions that allow us to eliminate the duality gap by introducing a perturbation in the primal objective function. As a by-product, we supply different extensions of well-known results concerning the subdifferential mapping.     

Duality for nonconvex absolute value programming and a characterization of linear max-min programs

In this paper a dual problem for nonconvex linear programs with absolute value functionals is constructed by means of a max-min problem involving bivalent variables. A relationship between the classical linear max-min problem and a linear program with absolute value functionals is developed. This program is then used to compute the duality gap between some max-min and min-max linear problems.     

绝对值方程算例分析

分别给出了绝对值方程存在唯一解的算例、存在2~n个解的算例、存在无穷多个解的算例以及无解的算例,并分析了这些算例中矩阵奇异值的特点.     

Duality and existence theory for nondifferentiable programming

Necessary and sufficient conditions of optimality are given for a nonlinear nondifferentiable program, where the constraints are defined via closed convex cones and their polars. These results are then used to obtain an existence theorem for the corresponding stationary point problem, under some convexity and regularity conditions on the functions involved, which also guarantee an optimal solution to the programming problem. Furthermore, a dual problem is defined, and a strong duality theorem is obtained under the assumption that the constraint sets of the primal and dual problems are nonempty.     

Existence theorems for boundary value problems in difference equations

In this research, we study linear difference equations with constant coefficients subject to boundary conditions. Necessary and/or sufficient conditions for the existence of a unique solution will be established. The proofs of the existence and uniqueness theorems are established by means of special types of determinants called Mosaic Vandermonde determinants.     

Nondifferentiable fractional programming with Hanson-Mond classes of functions

A class of nonlinear fractional programming problems is considered in which the numerator of the objective function involves a finite sum of square roots of quadratic forms. Inequality and equality constraints are considered. Necessary and sufficient optimality conditions and weak, strong, and converse duality theorems are established in the framework of the Hanson-Mond classes of functions.The author wishes to thank the unknown referee for the helpful comments that improved the quality of this paper.     

Sufficient conditions for the existence of multipliers and Lagrangian duality in abstract optimization problems

We consider the following optimization problem: in an abstract setX, find and elementx that minimizes a real functionf subject to the constraintsg(x)0 andh(x)=0, whereg andh are functions fromX into normed vector spaces. Assumptions concerning an overall convex structure for the problem in the image space, the existence of interior points in certain sets, and the normality of the constraints are formulated. A theorem of the alternative is proved for systems of equalities and inequalities, and an intrinsic multiplier rule and a Lagrangian saddle-point theorem (strong duality theorem) are obtained as consequences.     

Duality and saddle-point type optimality for generalized nonlinear fractional programming

In this paper, we establish two theorems of alternative with generalized subconvexlikeness. We introduce two dual models for a generalized fractional programming problem. Theorems of alternative are then applied to establish duality theorems and a saddle-point type optimality condition.