Linear Semi-infinite Programming Research Articles

Extending the mixed algebraic-analysis Fourier–Motzkin elimination method for classifying linear semi-infinite programmes

Motivated by a recent Basu–Martin–Ryan paper, we obtain a reduced primal-dual pair of a linear semi-infinite programming problem by applying an amended Fourier–Motzkin elimination method to the linear semi-infinite inequality system. The reduced primal-dual pair is equivalent to the original one in terms of consistency, optimal values and asymptotic consistency. Working with this reduced pair and reformulating a linear semi-infinite programme as a linear programme over a convex cone, we reproduce all the theorems that lead to the full eleven possible duality state classification theory. Establishing classification results with the Fourier–Motzkin method means that the two classification theorems for linear semi-infinite programming, 1969 and 1974, have been proved by new and exciting methods. We also show in this paper that the approach to study linear semi-infinite programming using Fourier–Motzkin elimination is not purely algebraic, it is mixed algebraic-analysis.

First order solutions in conic programming

We study the order of maximizers in linear conic programming (CP) as well as stability issues related to this. We do this by taking a semi-infinite view on conic programs: a linear conic problem can be formulated as a special instance of a linear semi-infinite program (SIP), for which characterizations of the stability of first order maximizers are well-known. However, conic problems are highly special SIPs, and therefore these general SIP-results are not valid for CP. We discuss the differences between CP and general SIP concerning the structure and results for stability of first order maximizers, and we present necessary and sufficient conditions for the stability of first order maximizers in CP.

Outer Limit of Subdifferentials and Calmness Moduli in Linear and Nonlinear Programming

With a common background and motivation, the main contributions of this paper are developed in two different directions. Firstly, we are concerned with functions, which are the maximum of a finite amount of continuously differentiable functions of n real variables, paying special attention to the case of polyhedral functions. For these max-functions, we obtain some results about outer limits of subdifferentials, which are applied to derive an upper bound for the calmness modulus of nonlinear systems. When confined to the convex case, in addition, a lower bound on this modulus is also obtained. Secondly, by means of a Karush---Kuhn---Tucker index set approach, we are also able to provide a point-based formula for the calmness modulus of the argmin mapping of linear programming problems, without any uniqueness assumption on the optimal set. This formula still provides a lower bound in linear semi-infinite programming. Illustrative examples are given.

Asymptotic optimality conditions for linear semi-infinite programming

In this paper, the classical KKT, complementarity and Lagrangian saddle-point conditions are generalized to obtain equivalent conditions characterizing the optimality of a feasible solution to a general linear semi-infinite programming problem without constraint qualifications. The method of this paper differs from the usual convex analysis methods and its main idea is rooted in some fundamental properties of linear programming.

Generalized corner optimal solution for LSIP: existence and numerical computation

In this paper, we consider general linear semi-infinite programming (LSIP) problems and study the existence and computation of optimal solutions at special generalized corner points called generalized ladder points (glp). We develop conditions, including an equivalent condition, under which glp optimal solutions exist. These results are fundamentally important to the ladder method for LSIP, which finds an optimal solution at a glp in the feasible region. For problems that do not have glp optimal solutions, we propose the addition of special artificial constraints to the constraint system of the problem to create a glp optimal solution. We present a ladder algorithm based on the maximum violation rule and an artificial ladder technique. Convergence results are provided with the support of some numerical tests.

Projection: A Unified Approach to Semi-Infinite Linear Programs and Duality in Convex Programming

Fourier-Motzkin elimination is a projection algorithm for solving finite linear programs. We extend Fourier-Motzkin elimination to semi-infinite linear programs, which are linear programs with finitely many variables and infinitely many constraints. Applying projection leads to new characterizations of important properties for primal-dual pairs of semi-infinite programs such as zero duality gap, feasibility, boundedness, and solvability. Extending the Fourier-Motzkin elimination procedure to semi-infinite linear programs yields a new classification of variables that is used to determine the existence of duality gaps. In particular, the existence of what the authors term dirty variables can lead to duality gaps. Our approach has interesting applications in finite-dimensional convex optimization. For example, sufficient conditions for a zero duality gap, such as the Slater constraint qualification, are reduced to guaranteeing that there are no dirty variables. This leads to completely new proofs of such sufficient conditions for zero duality.

A hybrid method for solving fuzzy semi-infinite linear programming problems

Abilities of fuzzy models in more adaptations with the real world phenomena causes that in this paper, we remodel semi-infinite linear programming problems as fuzzy problems which contained the crisp objective function and the infinite number of fuzzy constraints. Then, we introduce a new hybrid solution method for these fuzzy semi-infinite linear programming problems. This solution technique is a kind of cutting plane algorithm in which its sub-problems were solved by using the Zimmermann-method. Convergence of the presented algorithm is proved, and some numerical test examples are given and also the obtained results are compared.

Stochastic linear programming games with concave preferences

Abstract We study stochastic linear programming games: a class of stochastic cooperative games whose payoffs under any realization of uncertainty are determined by a specially structured linear program. These games can model a variety of settings, including inventory centralization and cooperative network fortification. We focus on the core of these games under an allocation scheme that determines how payoffs are distributed before the uncertainty is realized, and allows for arbitrarily different distributions for each realization of the uncertainty. Assuming that each player’s preferences over random payoffs are represented by a concave monetary utility functional, we prove that these games have a nonempty core. Furthermore, by establishing a connection between stochastic linear programming games, linear programming games and linear semi-infinite programming games, we show that an allocation in the core can be computed efficiently under some circumstances.

On Calmness of the Argmin Mapping in Parametric Optimization Problems

Recently, Canovas et al. presented an interesting result: the argmin mapping of a linear semi-infinite program under canonical perturbations is calm if and only if some associated linear semi-infinite inequality system is calm. Using classical tools from parametric optimization, we show that the if-direction of this condition holds in a much more general framework of optimization models, while the opposite direction may fail in the general case. In applications to special classes of problems, we apply a more recent result on the intersection of calm multifunctions.

Multiple-kernel-learning-based extreme learning machine for classification design

The extreme learning machine (ELM) is a new method for using single hidden layer feed-forward networks with a much simpler training method. While conventional kernel-based classifiers are based on a single kernel, in reality, it is often desirable to base classifiers on combinations of multiple kernels. In this paper, we propose the issue of multiple-kernel learning (MKL) for ELM by formulating it as a semi-infinite linear programming. We further extend this idea by integrating with techniques of MKL. The kernel function in this ELM formulation no longer needs to be fixed, but can be automatically learned as a combination of multiple kernels. Two formulations of multiple-kernel classifiers are proposed. The first one is based on a convex combination of the given base kernels, while the second one uses a convex combination of the so-called equivalent kernels. Empirically, the second formulation is particularly competitive. Experiments on a large number of both toy and real-world data sets (including high-magnification sampling rate image data set) show that the resultant classifier is fast and accurate and can also be easily trained by simply changing linear program.

A Dynamic Traveling Salesman Problem with Stochastic Arc Costs

We propose a dynamic traveling salesman problem (TSP) with stochastic arc costs motivated by applications, such as dynamic vehicle routing, in which the cost of a decision is known only probabilistically beforehand but is revealed dynamically before the decision is executed. We formulate this as a dynamic program (DP) and compare it to static counterparts to demonstrate the advantage of the dynamic paradigm over an a priori approach. We then apply approximate linear programming (ALP) to overcome the DP's curse of dimensionality, obtain a semi-infinite linear programming lower bound, and discuss its tractability. We also analyze a rollout version of the price-directed policy implied by our ALP and derive worst-case guarantees for its performance. Our computational study demonstrates the quality of a heuristically modified rollout policy using a computationally effective a posteriori bound.

A price-directed heuristic for the economic lot scheduling problem

The article formulates the well-known economic lot scheduling problem (ELSP) with sequence-dependent setup times and costs as a semi-Markov decision process. Using an affine approximation of the bias function, a semi-infinite linear program is obtained and a lower bound for the minimum average total cost rate is determined. The solution of this problem is directly used in a price-directed, dynamic heuristic to determine a good cyclic schedule. As the state space of the ELSP is non-trivial for the multi-product setting with setup times, the authors further illustrate how a lookahead version of the price-directed, dynamic heuristic can be used to construct and dynamically improve an approximation of the state space. Numerical results show that the resulting heuristic performs competitively with one reported in the literature.

γ-Active Constraints in Convex Semi-Infinite Programming

In this article, we extend the definition of γ-active constraints for linear semi-infinite programming to a definition applicable to convex semi-infinite programming, by two approaches. The first approach entails the use of the subdifferentials of the convex constraints at a point, while the second approach is based on the linearization of the convex inequality system by means of the convex conjugates of the defining functions. By both these methods, we manage to extend the results on γ-active constraints from the linear case to the convex case.

A Unifying Approximate Dynamic Programming Model for the Economic Lot Scheduling Problem

We formulate the well-known economic lot scheduling problem (ELSP) with sequence-dependent setup times and costs as a semi-Markov decision process. Using an affine approximation of the bias function, we obtain a semi-infinite linear program determining a lower bound for the minimum average cost rate. Under a very mild condition, we can reduce this problem to a relatively small convex quadratically constrained linear problem by exploiting the structure of the objective function and the state space. This problem is equivalent to the lower bound problem derived by Dobson [Dobson G (1992) The cyclic lot scheduling problem with sequence-dependent setups. Oper. Res. 40:736–749] and reduces to the well-known lower bound problem introduced in Bomberger [Bomberger EE (1966) A dynamic programming approach to a lot size scheduling problem. Management Sci. 12:778–784] for sequence-dependent setups. We thus provide a framework that unifies previous work, and opens new paths for future research on tighter lower bounds and dynamic heuristics.

Calmness Modulus of Linear Semi-infinite Programs

Our main goal is to compute or estimate the calmness modulus of the argmin mapping of linear semi-infinite optimization problems under canonical perturbations, i.e., perturbations of the objective function together with continuous perturbations of the right-hand side of the constraint system (with respect to an index ranging in a compact Hausdorff space). Specifically, we provide a lower bound on the calmness modulus for semi-infinite programs with unique optimal solution which turns out to be the exact modulus when the problem is finitely constrained. The relationship between the calmness of the argmin mapping and the same property for the (sub)level set mapping (with respect to the objective function), for semi-infinite programs and without requiring the uniqueness of the nominal solution, is explored, too, providing an upper bound on the calmness modulus of the argmin mapping. When confined to finitely constrained problems, we also provide a computable upper bound as it only relies on the nominal data and parameters, not involving elements in a neighborhood. Illustrative examples are provided.

On the sufficiency of finite support duals in semi-infinite linear programming

We consider semi-infinite linear programs with countably many constraints indexed by the natural numbers. When the constraint space is the vector space of all real valued sequences, we show that the finite support (Haar) dual is equivalent to the algebraic Lagrangian dual of the linear program. This settles a question left open by Anderson and Nash (1987). This result implies that if there is a duality gap between the primal linear program and its finite support dual, then this duality gap cannot be closed by considering the larger space of dual variables that define the algebraic Lagrangian dual. However, if the constraint space corresponds to certain subspaces of all real-valued sequences, there may be a strictly positive duality gap with the finite support dual, but a zero duality gap with the algebraic Lagrangian dual.

The design of mission-based component test plans for series connection of subsystems

This article analyzes the mission-based component testing problem of devices which consist of series connection of 1-out-of-n subsystems or series connection of m-out-of-n subsystems. The device is designed to perform a mission that has a random sequence of phases with random durations. It is assumed that the deterioration of the components of the system is modulated by the mission process in such a way that the component failure rates depend on the phase that is performed. The objective is to find optimal component test times that yield desired levels of system reliability. An algorithmic procedure that is based on a column generation technique and d.c. programming is presented. This procedure eventually solves a semi-infinite linear program and it is illustrated by numerical examples. The existence of optimal component test times is discussed and sufficient conditions for the feasibility of the underlying semi-infinite linear programming model are determined.

Robust GRAPPA reconstruction using sparse multi-kernel learning with least squares support vector regression

Accuracy of interpolation coefficients fitting to the auto-calibrating signal data is crucial for k-space-based parallel reconstruction. Both conventional generalized autocalibrating partially parallel acquisitions (GRAPPA) reconstruction that utilizes linear interpolation function and nonlinear GRAPPA (NLGRAPPA) reconstruction with polynomial kernel function are sensitive to interpolation window and often cannot consistently produce good results for overall acceleration factors. In this study, sparse multi-kernel learning is conducted within the framework of least squares support vector regression to fit interpolation coefficients as well as to reconstruct images robustly under different subsampling patterns and coil datasets. The kernel combination weights and interpolation coefficients are adaptively determined by efficient semi-infinite linear programming techniques. Experimental results on phantom and in vivo data indicate that the proposed method can automatically achieve an optimized compromise between noise suppression and residual artifacts for various sampling schemes. Compared with NLGRAPPA, our method is significantly less sensitive to the interpolation window and kernel parameters.

A ladder method for linear semi-infinite programming

This paper presents a new method for linear semi-infinite programming. With theintroduction of the so-called generalized ladder point, a ladder method forlinear semi-infinite programming is developed. This work includes thegeneralization of the inclusive cone version of the fundamental theorem of linearprogramming and the extension of a linear programming ladder algorithm. The extendedladder algorithm finds a generalized ladder point optimal solution of the linearsemi-infinite programming problem, which is approximated by a sequence of ladder points.Simple convergence properties are provided. The algorithm is tested by solving a numberof linear semi-infinite programming examples. These numerical results indicate that thealgorithm is very efficient when compared with other methods.

On solving a class of linear semi-infinite programming by SDP method

In this paper, we present a new method to solve linear semi-infinite programming. This method bases on the fact that the nonnegative polynomial on could be turned into a positive semi-definite system, so we can use the nonnegative polynomials to approximate the semi-infinite constraint. Furthermore, we set up an approximate programming for the primal linear semi-infinite programming, and obtain an error bound between two programming problems. Numerical results show that our method is efficient.