The linear complementarity problem as a separable bilinear program

The nonmonotone linear complementarity problem (LCP) is formulated as a bilinear program with separable constraints and an objective function that minimizes a natural error residual for the LCP. A linear-programming-based algorithm applied to the bilinear program terminates in a finite number of steps at a solution or stationary point of the problem. The bilinear algorithm solved 80 consecutive cases of the LCP formulation of the knapsack feasibility problem ranging in size between 10 and 3000, with almost constant average number of major iterations equal to four.This material is based on research supported by Air Force Office of Scientific Research Grant F49620-94-1-0036 and National Science Foundation Grants CCR-9322479 and CDA-9024618.     

Polynomial dual network simplex algorithms

We show how to use polynomial and strongly polynomial capacity scaling algorithms for the transshipment problem to design a polynomial dual network simplex pivot rule. Our best pivoting strategy leads to an O(m ² logn) bound on the number of pivots, wheren andm denotes the number of nodes and arcs in the input network. If the demands are integral and at mostB, we also give an O(m(m+n logn) min(lognB, m logn))-time implementation of a strategy that requires somewhat more pivots.Research supported by AFOSR-88-0088 through the Air Force Office of Scientific Research, by NSF grant DOM-8921835 and by grants from Prime Computer Corporation and UPS.Research supported by NSF Research Initiation Award CCR-900-8226, by U.S. Army Research Office Grant DAAL-03-91-G-0102, and by ONR Contract N00014-88-K-0166.Research supported in part by a Packard Fellowship, an NSF PYI award, a Sloan Fellowship, and by the National Science Foundation, the Air Force Office of Scientific Research, and the Office of Naval Research, through NSF grant DMS-8920550.     

Error bounds for nondifferentiable convex inequalities under a strong Slater constraint qualification

A global error bound is given on the distance between an arbitrary point in then-dimensional real spaceR ⁿ and its projection on a nonempty convex set determined bym convex, possibly nondifferentiable, inequalities. The bound is in terms of a natural residual that measures the violations of the inequalities multiplied by a new simple condition constant that embodies a single strong Slater constraint qualification (CQ) which implies the ordinary Slater CQ. A very simple bound on the distance to the projection relative to the distance to a point satisfying the ordinary Slater CQ is given first and then used to derive the principal global error bound. This material is based on research supported by National Science Foundation Grant CCR-9322479 and Air Force Office of Scientific Research grant F49620-97-1-0326.     

Approximations of pseudo-Boolean functions; applications to game theory

This paper studies the approximation of pseudo-Boolean functions by linear functions and more generally by functions of (at most) a specified degree. Here a pseudo-Boolean function means a real valued function defined on {0,1} ⁿ , and its degree is that of the unique multilinear polynomial that expresses it; linear functions are those of degree at most one. The approximation consists in choosing among all linear functions the one which is closest to a given function, where distance is measured by the Euclidean metric onR ²ⁿ . A characterization of the best linear approximation is obtained in terms of the average value of the function and its first derivatives. This leads to an explicit formula for computing the approximation from the polynomial expression of the given function. These results are later generalized to handle approximations of higher degrees, and further results are obtained regarding the interaction of approximations of different degrees. For the linear case, a certain constrained version of the approximation problem is also studied. Special attention is given to some important properties of pseudo-Boolean functions and the extent to which they are preserved in the approximation. A separate section points out the relevance of linear approximations to game theory and shows that the well known Banzhaf power index and Shapley value are obtained as best linear approximations of the game (each in a suitably defined sense).Supported by the Air Force Office of Scientific Research (under grant number AFOSR 89-0512 and AFOSR 90-0008 to Rutgers University), as well as the National Science Foundation (under grant number DMS 89-06870).     

On computing majority by comparisons

The elements of a finite setX (of odd cardinalityn) are divided into two (as yet unknown) classes and a member of the larger class is to be identified. The basic operation is to test whether two objects are in the same class. We show thatn-B(n) comparisons are necessary and sufficient in worst case, whereB(n) is the number of 1's in the binary expansion ofn.Supported in part by NSF grant DMS87 03541 and Air Force Office of Scientific Research grant AFOSR-0271.     

An algorithm for nonlinear optimization using linear programming and equality constrained subproblems

This paper describes an active-set algorithm for large-scale nonlinear programming based on the successive linear programming method proposed by Fletcher and Sainz de la Maza [10]. The step computation is performed in two stages. In the first stage a linear program is solved to estimate the active set at the solution. The linear program is obtained by making a linear approximation to the ₁ penalty function inside a trust region. In the second stage, an equality constrained quadratic program (EQP) is solved involving only those constraints that are active at the solution of the linear program. The EQP incorporates a trust-region constraint and is solved (inexactly) by means of a projected conjugate gradient method. Numerical experiments are presented illustrating the performance of the algorithm on the CUTEr [1, 15] test set.This author was supported by Air Force Office of Scientific Research grant F49620-00-1-0162, Army Research Office Grant DAAG55-98-1-0176, and National Science Foundation grant INT-9726199.This author was supported in part by the EPSRC grant GR/R46641.These authors were supported by National Science Foundation grants CCR-9987818, ATM-0086579 and CCR-0219438 and Department of Energy grant DE-FG02-87ER25047-A004.Report OTC 2002/4, Optimization Technology CenterTo Roger Fletcher, with respect and admiration     

A critical index algorithm for nearest point problems on simplicial cones

We consider the linear complementarity problem (q, M) in whichM is a positive definite symmetric matrix of ordern. This problem is equivalent to a nearest point problem [; b] in which = {A.₁,, A. _n } is a basis for R ⁿ ,b is a given point in R ⁿ ; and it is required to find the nearest point in the simplicial cone Pos() tob. We develop an algorithm for solving the linear complementarity problem (q, M) or the equivalent nearest point problem [; b]. Computational experience in comparison with an existing algorithm is presented.Research effort partially supported by the Air Force Office of Scientific Research. Air Force Systems Command, USAF, under grant No. AFOSR 78-3646. The United States Government is authorized to reproduce and distribute reprints for governmental purposes, not withstanding any copyright notation hereon.     

A random 1-011-011-01algorithm for depth first search

In this paper we present a fast parallel algorithm for constructing a depth first search tree for an undirected graph. The algorithm is anRNC algorithm, meaning that it is a probabilistic algorithm that runs in polylog time using a polynomial number of processors on aP-RAM. The run time of the algorithm isO(T _MM(n) log³ n), and the number of processors used isP _MM (n) whereT _MM(n) andP _MM(n) are the time and number of processors needed to find a minimum weight perfect matching on ann vertex graph with maximum edge weightn.This research was done while the first author was visiting the Mathematical Research Institute in Berkeley. Research supported in part by NSF grant 8120790.Supported by Air Force Grant AFOSR-85-0203A.     

Adaptive Lanczos methods for recursive condition estimation

Estimates for the condition number of a matrix are useful in many areas of scientific computing, including: recursive least squares computations, optimization, eigenanalysis, and general nonlinear problems solved by linearization techniques where matrix modification techniques are used. The purpose of this paper is to propose anadaptiveLanczosestimator scheme, which we callale, for tracking the condition number of the modified matrix over time. Applications to recursive least squares (RLS) computations using the covariance method with sliding data windows are considered.ale is fast for relatively smalln-parameter problems arising in RLS methods in control and signal processing, and is adaptive over time, i.e., estimates at timet are used to produce estimates at timet+1. Comparisons are made with other adaptive and non-adaptive condition estimators for recursive least squares problems. Numerical experiments are reported indicating thatale yields a very accurate recursive condition estimator.Research supported by the US Air Force under grant no. AFOSR-88-0285.Research supported by the US Army under grant no. DAAL03-90-G-105.Research supported by the US Air Force under grant no. AFOSR-88-0285.     

On the finite volume element method

Summary The finite volume element method (FVE) is a discretization technique for partial differential equations. It uses a volume integral formulation of the problem with a finite partitioning set of volumes to discretize the equations, then restricts the admissible functions to a finite element space to discretize the solution. this paper develops discretization error estimates for general selfadjoint elliptic boundary value problems with FVE based on triangulations with linear finite element spaces and a general type of control volume. We establishO(h) estimates of the error in a discreteH ¹ semi-norm. Under an additional assumption of local uniformity of the triangulation the estimate is improved toO(h ²). Results on the effects of numerical integration are also included.This research was sponsored in part by the Air Force Office of Scientific Research under grant number AFOSR-86-0126 and the National Science Foundation under grant number DMS-8704169. This work was performed while the author was at the University of Colorado at Denver     

On optimality conditions for structured families of nonlinear programming problems

Optimality conditions for families of nonlinear programming problems inR ⁿ are studied from a generic point of view. The objective function and some of the constraints are assumed to depend on a parameter, while others are held fixed. Techniques of differential topology are used to show that under suitable conditions, certain strong second-order conditions are necessary for optimality except possibly for parameter values lying in a negligible set.Research sponsored, in part, by the Air Force Office of Scientific Research, under grants number 77-3204 and 79-0120.     

Error bounds for strongly convex programs and (super)linearly convergent iterative schemes for the least 2-norm solution of linear programs

Given an arbitrary point (x, u) inR ⁿ × R ₊ ^m , we give bounds on the Euclidean distance betweenx and the unique solution to a strongly convex program in terms of the violations of the Karush-Kuhn-Tucker conditions by the arbitrary point (x, u). These bounds are then used to derive linearly and superlinearly convergent iterative schemes for obtaining the unique least 2-norm solution of a linear program. These schemes can be used effectively in conjunction with the successive overrelaxation (SOR) methods for solving very large sparse linear programs.Sponsored by the United States Army under Contract No. DAAG29-80-C-0041. This material is based on research sponsored by National Science Foundation Grant No. DCR-8420963 and Air Force Office of Scientific Research Grant No. AFOSR-ISSA-85-00080.On leave from CRAI, via Bernini 5, Rende, Cosenza, Italy.     

Computational complexity of LCPs associated with positive definite symmetric matrices

Murty in a recent paper has shown that the computational effort required to solve a linear complementarity problem (LCP), by either of the two well known complementary pivot methods is not bounded above by a polynomial in the size of the problem. In that paper, by constructing a class of LCPs—one of ordern forn 2—he has shown that to solve the problem of ordern, either of the two methods goes through 2 ⁿ pivot steps before termination.However that paper leaves it as an open question to show whether or not the same property holds if the matrix,M, in the LCP is positive definite and symmetric. The class of LCPs in whichM is positive definite and symmetric is of particular interest because of the special structure of the problems, and also because they appear in many practical applications.In this paper, we study the computational growth of each of the two methods to solve the LCP, (q, M), whenM is positive definite and symmetric and obtain similar results.This research is partially supported by Air Force Office of Scientific Research, Air Force Number AFOSR-78-3646. The United States Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright notation thereon.     

A generalization of Riemann's method for partial differential equations

Summary A bilinear divergence identity is obtained, which differs from the usualLagrange divergence identity employed byRiemann. In the case of two independent variables, this new identity is used to unify the treatment ofCauchy's problem for hyperbolic equations, the initial value problem for parabolic equations, and theDirichlet problem for elliptic equations. This research was supported in whole or in part by the United States Air Force under Contract N^o. AF18(600)-573 monitored by the Office of Scientific Research, Air Research and Development Command.     

Error analysis of a pairwise summation algorithm to compute the sample variance

Summary We give an error analysis of an algorithm for computing the sample variance due to Chan, Golub, and LeVeque [The American Statistician 7 (1983), pp. 242–247]. It is shown that this algorithm is numerically stable. The algorithm computes the sample variance (and the sample mean) using just one pass through the sample data. It is amenable to pairwise summation and thus requires onlyO(logn) parallel steps.Research supported by the Air Force Office of Scientific Research under grant no. AFOSR-88-0161 and by the Office of Naval Research under grant no. N00024-85-C-6041     

Finding an interior point in the optimal face of linear programs

We study the problem of finding a point in the relative interior of the optimal face of a linear program. We prove that in the worst case such a point can be obtained in O(n ³ L) arithmetic operations. This complexity is the same as the complexity for solving a linear program. We also show how to find such a point in practice. We report and discuss computational results obtained for the linear programming problems in the NETLIB test set.Research supported in part by NSF Grant CCR-8810107, CCR-9019469 and a grant from GTE Laboratories.Research supported in part by NSF Grant DDM-8922636 and NSF Coop. Agr. No. CCR-8809615 through Rice University.     

Misclassification minimization

The problem of minimizing the number of misclassified points by a plane, attempting to separate two point sets with intersecting convex hulls inn-dimensional real space, is formulated as a linear program with equilibrium constraints (LPEC). This general LPEC can be converted to an exact penalty problem with a quadratic objective and linear constraints. A Frank-Wolfe-type algorithm is proposed for the penalty problem that terminates at a stationary point or a global solution. Novel aspects of the approach include: (i) A linear complementarity formulation of the step function that counts misclassifications, (ii) Exact penalty formulation without boundedness, nondegeneracy or constraint qualification assumptions, (iii) An exact solution extraction from the sequence of minimizers of the penalty function for a finite value of the penalty parameter for the general LPEC and an explicitly exact solution for the LPEC with uncoupled constraints, and (iv) A parametric quadratic programming formulation of the LPEC associated with the misclassification minimization problem.This material is based on research supported by Air Force Office of Scientific Research Grant F49620-94-1-0036 and National Science Foundation Grants CCR-9101801 and CDA-9024618.     

On the stability of bilinear-constant velocity-pressure finite elements

Summary An analysis of the Babuka stability of bilinear/constant finite element pairs for viscous flow calculations is given. An unstable mode not of the checkerboard type is given for which the stability constant turns out to beO(h). Thus, the indicated spaces are not stable in general for numerical calculation.Work supported by U.S. Air Force Office of Scientific Research under grant AF-AFOSR-82-0213     

Optimizing weakly triangulated graphs

A graph is weakly triangulated if neither the graph nor its complement contains a chordless cycle with five or more vertices as an induced subgraph. We use a new characterization of weakly triangulated graphs to solve certain optimization problems for these graphs. Specifically, an algorithm which runs inO((n + e)n ³) time is presented which solves the maximum clique and minimum colouring problems for weakly triangulated graphs; performing the algorithm on the complement gives a solution to the maximum stable set and minimum clique covering problems. Also, anO((n + e)n ⁴) time algorithm is presented which solves the weighted versions of these problems.The author acknowledges the support of an N.S.E.R.C. Canada postgraduate scholarship.The author acknowledges the support of the U.S. Air Force Office of Scientific Research under grant number AFOSR 0271 to Rutgers University.     

An effective algorithm for minimization

An algorithm is proposed for minimizing certain niceC ² functionsf onE _n assuming only a computational knowledge off andf. It is shown that the algorithm provides global convergence at a rate which is eventually superlinear and possibly quadratic. The algorithm is purely algebraic and does not require the minimization of any functions of one variable.Numerical computation on specific problems with as many as six independent variables has shown that the method compares very favorably with the best of the other known methods. The method is compared with theFletcher andPowell method for a simple two dimensional test problem and for a six dimensional problem arising in control theory.Supported by Air Force grant AF-AFO SR-93 7-65 and Boeing Scientific Research Laboratories.