A linear programming algorithm for curve fitting in the L∞ norm

The L_∞ norm has been widely studied as a criterion for curve fitting problems. This paper presents an algorithm to solve discrete approximation problems in the L_∞ norm. The algorithm is a special-purpose linear programming method using the dual form of the problem, which employs a reduced basis and multiple pivots. Results of the computational experience with a computer code version of the algorithm are presented.     

Near boundary behavior of primal—dual potential reduction algorithms for linear programming

This paper is concerned with selection of the-parameter in the primal—dual potential reduction algorithm for linear programming. Chosen from [n + , ), the level of determines the relative importance placed on the centering vs. the Newton directions. Intuitively, it would seem that as the iterate drifts away from the central path towards the boundary of the positive orthant, must be set close ton + . This increases the relative importance of the centering direction and thus helps to ensure polynomial convergence. In this paper, we show that this is unnecessary. We find for any iterate that can be sometimes chosen in a wide range [n + , ) while still guaranteeing the currently best convergence rate of O( L) iterations. This finding is encouraging since in practice large values of have resulted in fast convergence rates. Our finding partially complements the recent result of Zhang, Tapia and Dennis (1990) concerning the local convergence rate of the algorithm.Research supported in part by NSF Grant DDM-8922636.     

A “build-down” scheme for linear programming

We propose a build-down scheme for Karmarkar's algorithm and the simplex method for linear programming. The scheme starts with an optimal basis candidate set including all columns of the constraint matrix, then constructs a dual ellipsoid containing all optimal dual solutions. A pricing rule is developed for checking whether or not a dual hyperplane corresponding to a column intersects the containing ellipsoid. If the dual hyperplane has no intersection with the ellipsoid, its corresponding column will not appear in any of the optimal bases, and can be eliminated from. As these methods iterate, is eventually built-down to a set that contains only the optimal basic columns.     

A globally convergent primal—dual interior point algorithm for convex programming

In this paper, we study the global convergence of a large class of primal—dual interior point algorithms for solving the linearly constrained convex programming problem. The algorithms in this class decrease the value of a primal—dual potential function and hence belong to the class of so-called potential reduction algorithms. An inexact line search based on Armijo stepsize rule is used to compute the stepsize. The directions used by the algorithms are the same as the ones used in primal—dual path following and potential reduction algorithms and a very mild condition on the choice of the centering parameter is assumed. The algorithms always keep primal and dual feasibility and, in contrast to the polynomial potential reduction algorithms, they do not need to drive the value of the potential function towards — in order to converge. One of the techniques used in the convergence analysis of these algorithms has its root in nonlinear unconstrained optimization theory.Research supported in part by NSF Grant DDM-9109404.     

A Lagrange–Newton algorithm for sparse nonlinear programming

Mathematical Programming - The sparse nonlinear programming (SNP) problem has wide applications in signal and image processing, machine learning and finance, etc. However, the computational...     

A smoothing projected Newton-type algorithm for semi-infinite programming

This paper presents a smoothing projected Newton-type method for solving the semi-infinite programming (SIP) problem. We first reformulate the KKT system of the SIP problem into a system of constrained nonsmooth equations. Then we solve this system by a smoothing projected Newton-type algorithm. At each iteration only a system of linear equations needs to be solved. The feasibility is ensured via the aggregated constraint under some conditions. Global and local superlinear convergence of this method is established under some standard assumptions. Preliminary numerical results are reported. Qi’s work is supported by the Hong Kong Research Grant Council. Ling’s work was supported by the Zhejiang Provincial National Science Foundation of China (Y606168). Tong’s work was done during her visit to The Hong Kong Polytechnic University. Her work is supported by the NSF of China (60474070) and the Technology Grant of Hunan (06FJ3038). Zhou’s work is supported by Australian Research Council.     

A dual variant of Benson’s “outer approximation algorithm” for multiple objective linear programming

Outcome space methods construct the set of nondominated points in the objective (outcome) space of a multiple objective linear programme. In this paper, we employ results from geometric duality theory for multiple objective linear programmes to derive a dual variant of Benson’s “outer approximation algorithm” to solve multiobjective linear programmes in objective space. We also suggest some improvements of the original version of the algorithm and prove that solving the dual provides a weight set decomposition. We compare both algorithms on small illustrative and on practically relevant examples.     

A unified Douglas–Rachford algorithm for generalized DC programming

Journal of Global Optimization - We consider a class of generalized DC (difference-of-convex functions) programming, which refers to the problem of minimizing the sum of two convex (possibly...     

Combined phase I—phase II methods of feasible directions

This paper presents several new algorithms, generalizing feasible directions algorithms, for the nonlinear programming problem, min{f ⁰ (z) f ^j (z) 0,j = 1, 2, ,m}. These new algorithms do not require an initial feasible point. They automatically combine the operations of initialization (phase I) and optimization (phase II).Research sponsored by the National Science Foundation (RANN) Grant ENV76-04264 and the National Science Foundation Grant ENG73-08214-A01.     

Optimality theory for semi-infinite linear programming ∗

This paper presents an exhaustive approach to optimality theory in semi-infinite linear programming, placing a special emphasis on generality. After surveying optimality conditions for general problems, a detailed analysis is made of problems in which the coefficients are continuous functions of a parameter which varies on a compact set, adopting a feasible directions approach. Lastly, the case of analytical coefficients over an interval is considered in some detail.     

Computer programs for non‐linear programming

In this paper we consider the problem of maximizing a non‐linear or linear objective function subject to non‐linear and/or linear constraints. The approach used is an adaptive random search with some non‐random searches built‐in. The algorithm begins with a given point which is replaced by another point if the latter satisfies each of the constraints and results in a bigger functional value. The process of moving from one point to a better point is repeated many times. The value of each of the coordinates of the next point is determined by one of several ways; for example, a coordinate is sometimes forced to have the same value as the value of the corresponding coordinate of the current feasible point. In this algorithm, a candidate point receives no further computational considerations as soon as it is found to be unfeasible; this makes the algorithm general. Computer programs illustrating the details of the new algorithm are given and computational results of two numerical test problems from the literature are presented. Optimality was reached in each of these two problems.     

General purpose heuristics for integer programming—Part I

In spite of the many special purpose heuristics for specific classes of integer programming (IP) problems, there are few developments that focus on general purpose integer programming heuristics. This stems partly from the perception that general purpose methods are likely to be less effective than specialized procedures for specific problems, and partly from the perception that there is no unifying theoretical basis for creating general purpose heuristics. Still, there is a general acknowledgment that methods which are not limited to solving IP problems on a class by class basis, but which apply to a broader range of problems, have significant value. We show that certain ideas proposed in the 1970s, which are often overlooked, can be reformulated and linked with more recent developments to give a useful theoretical framework for generating general purpose IP heuristics. This framework, which has the appeal of being highly visual, makes use of cutting plane derivations that also give a natural basis for marrying heuristics with exact branch and cut methods for integer programming problems.     

A stable primal–dual approach for linear programming under nondegeneracy assumptions

This paper studies a primal–dual interior/exterior-point path-following approach for linear programming that is motivated on using an iterative solver rather than a direct solver for the search direction. We begin with the usual perturbed primal–dual optimality equations. Under nondegeneracy assumptions, this nonlinear system is well-posed, i.e. it has a nonsingular Jacobian at optimality and is not necessarily ill-conditioned as the iterates approach optimality. Assuming that a basis matrix (easily factorizable and well-conditioned) can be found, we apply a simple preprocessing step to eliminate both the primal and dual feasibility equations. This results in a single bilinear equation that maintains the well-posedness property. Sparsity is maintained. We then apply either a direct solution method or an iterative solver (within an inexact Newton framework) to solve this equation. Since the linearization is well posed, we use affine scaling and do not maintain nonnegativity once we are close enough to the optimum, i.e. we apply a change to a pure Newton step technique. In addition, we correctly identify some of the primal and dual variables that converge to 0 and delete them (purify step). We test our method with random nondegenerate problems and problems from the Netlib set, and we compare it with the standard Normal Equations NEQ approach. We use a heuristic to find the basis matrix. We show that our method is efficient for large, well-conditioned problems. It is slower than NEQ on ill-conditioned problems, but it yields higher accuracy solutions.     

A hybrid of genetic algorithm and particle swarm optimization for solving bi-level linear programming problem – A case study on supply chain model

The main goal of supply chain management is to coordinate and collaborate the supply chain partners seamlessly. On the other hand, bi-level linear programming is a technique for modeling decentralized decision. It consists of the upper level and lower level objectives. Thus, this paper intends to apply bi-level linear programming to supply chain distribution problem and develop an efficient method based on hybrid of genetic algorithm (GA) and particle swarm optimization (PSO). The performance of the proposed method is ascertained by comparing the results with GA and PSO using four problems in the literature and a supply chain distribution model.     

A note on “A new method for solving fully fuzzy linear programming problems”

This note shows that solving fully fuzzy linear programming (FFLP) model presented by Kumar et al. [A. Kumar, J. Kaur, P. Singh, A new method for solving fully fuzzy linear programming problems, Appl. Math. Model. 35 (2011) 817–823] needs some corrections to make the model well in general. A new version is provided in this note. A simple example is also presented to demonstrate the new form.     

An algorithm to perform a complete right-hand-side parametrical analysis for a 0–1-integer linear programming problem

We designed and implemented an algorithm to solve the continuous right-hand side parametric 0–1-Integer Linear Programming (ILP) problem, that is to solve a family of 0–1-ILP problems in which the problems are related by having identical objective and matrix coefficients. Our algorithm works by choosing an appropiate finite sequence of non-parametric 0–1-Mixed Integer Linear Programming (MILP) problems in order to obtain a complete parametrical analysis. The algorithm may be implemented by using any software capable of solving MILP problems.     

A customized Douglas–Rachford splitting algorithm for separable convex minimization with linear constraints

We consider applying the Douglas–Rachford splitting method (DRSM) to the convex minimization problem with linear constraints and a separable objective function. The dual application of DRSM has been well studied in the literature, resulting in the well known alternating direction method of multipliers (ADMM). In this paper, we show that the primal application of DRSM in combination with an appropriate decomposition can yield an efficient structure-exploiting algorithm for the model under consideration, whose subproblems could be easier than those of ADMM. Both the exact and inexact versions of this customized DRSM are studied; and their numerical efficiency is demonstrated by some preliminary numerical results.     

A linear time algorithm for the Koopmans–Beckmann QAP linearization and related problems

An instance of the quadratic assignment problem (QAP) with cost matrix $Q$ is said to be linearizable if there exists an instance of the linear assignment problem (LAP) with cost matrix $C$ such that for each assignment, the QAP and LAP objective function values are identical. The QAP linearization problem can be solved in $O\left(n^4\right)$ time. However, for the special cases of Koopmans–Beckmann QAP and the multiplicative assignment problem the input size is of $\Omega \left(n^2\right)$. We show that the QAP linearization problem for these special cases can be solved in $O\left(n^2\right)$ time. For symmetric Koopmans–Beckmann QAP, Bookhold [I. Bookhold, A contribution to quadratic assignment problems, Optimization 21 (1990) 933–943.] gave a sufficient condition for linearizability and raised the question if the condition is necessary. We show that Bookhold’s condition is also necessary for linearizability of symmetric Koopmans–Beckmann QAP.