An ellipsoid algorithm for nonlinear programming

We investigate an ellipsoid algorithm for nonlinear programming. After describing the basic steps of the algorithm, we discuss its computer implementation and present a method for measuring computational efficiency. The computational results obtained from experimenting with the algorithm are discussed and the algorithm's performance is compared with that of a widely used commercial code. This research was supported in part by The National Science Foundation, Grant No. MCS78-02096.     

Variable metric relaxation methods,part II: The ellipsoid method

The deepest, or least shallow, cut ellipsoid method is a polynomial (time and space) method which finds an ellipsoid, representable by polynomial space integers, such that the maximal ellipsoidal distance relaxation method using this fixed ellipsoid is polynomial: this is equivalent to finding a linear transforming such that the maximal distance relaxation method of Agmon, Motzkin and Schoenberg in this transformed space is polynomial. If perfect arithmetic is used, then the sequence of ellipsoids generated by the method converges to a set of ellipsoids, which share some of the properties of the classical Hessian at an optimum point of a function; and thus the ellipsoid method is quite analogous to a variable metric quasi-Newton method. This research was supported in part by the F.C.A.C. of Quebec, and the N.S.E.R.C. of Canada under Grant A 4152.     

A class of rank-two ellipsoid algorithms for convex programming

In this paper, we develop a method for constructing minimum volume ellipsoids containing a wedge-shaped subset of a given ellipsoid. This construction yields a class of ellipsoid algorithms for convex programming that use rank-two update formulae. Research supported by the National Science Foundation, Grant No. MC582-01790.     

A class of methods for linear programming

A class of methods is presented for solving standard linear programming problems. Like the simplex method, these methods move from one feasible solution to another at each iteration, improving the objective function as they go. Each such feasible solution is also associated with a basis. However, this feasible solution need not be an extreme point and the basic solution corresponding to the associated basis need not be feasible. Nevertheless, an optimal solution, if one exists, is found in a finite number of iterations (under nondegeneracy). An important example of a method in the class is the reduced gradient method with a slight modification regarding selection of the entering variable.     

An interior multiobjective primal-dual linear programming algorithm using approximated gradients and sequential generation of anchor points

An algorithm for addressing multiple objective linear programming (MOLP) problems is presented. The algorithm modifies the path-following primal-dual algorithm to MOLP problems by using the single objective algorithm to generate interior search directions and later combine them to derive a single direction along which to step to the next iterate. Combining the different interior search directions is done by interacting with a Decision Maker (DM) to obtain locally-relevant preference information for the value vectors along these directions. This preference information is then used to derive an approximation to the gradient of an implicity-known utility function, and using a projection of this gradient provides a direction gradient of an implicitly-known utility function, and using a projection of this gradient provides a direction vector along which we step to the next iterate. At each iteration the algorithm also generates boundary points that aid in deriving the combined search direction. We refer to these boundary points, generated sequentially during the process, as anchor points that serve as candidate solutions at which to terminate the iterative process.     

A primal conical linear programming algorithm

This paper completes the treatment of the conical approach to linear programming, introducing a conical primal algorithm of linear programming. After some recalls and improvements of a previous paper dealing with such an approach, the algorithm is defined. A first convergence result is then proved, which, along with a series of lemmata, allows to prove that the algorithm terminates in a finite number of steps     

An implementation of Karmarkar's algorithm for linear programming

This paper describes the implementation of power series dual affine scaling variants of Karmarkar's algorithm for linear programming. Based on a continuous version of Karmarkar's algorithm, two variants resulting from first and second order approximations of the continuous trajectory are implemented and tested. Linear programs are expressed in an inequality form, which allows for the inexact computation of the algorithm's direction of improvement, resulting in a significant computational advantage. Implementation issues particular to this family of algorithms, such as treatment of dense columns, are discussed. The code is tested on several standard linear programming problems and compares favorably with the simplex codeMinos 4.0.     

Combining a path method and parametric linear programming for the computation of competitive equilibria

A path-following philosophy (continuation method, global Newton method) is used to compute equilibria for piecewise linear economies while taking advantage of the linear structure of the model. The existence of a path leading through certain faces of a polyhedral set to an equilibrium point is demonstrated. Computational experience is reported which indicates that this method is promising for models dealing with many commodities and relatively few consumers.Most of this paper has been extracted from the author's doctoral dissertation for the Department of Operations Research at Stanford University; the author would like to express indebtedness to his advisor, R. Wilson. Major revisions were made while the author was at Bell Laboratories in Whippany, New Jersey.     

A decomposition algorithm for quadratic programming

A decomposition algorithm using Lemke's method is proposed for the solution of quadratic programming problems having possibly unbounded feasible regions. The feasible region for each master program is a generalized simplex of minimal size. This property is maintained by a dropping procedure which does not affect the finiteness of the convergence. The details of the matrix transformations associated with an efficient implementation of the algorithm are given. Encouraging preliminary computational experience is presented.     

The pivot and probe algorithm for solving a linear program

In [7] we defined acandidate constraint as one which, for at least one pivot step, contains a potential pivot, discovered that most constraints are never candidate, and devised a modification of the simplex method in which only constraints which are currently candidates are updated. In this paper we present another way to take advantage of this fact. We begin by solving a relaxed linear program consisting of the constraints of the original problem which are initially candidates. Its solution gives an upper bound to the value of the original problem. We also introduce the idea of a probe, that is, a line segment joining two vectors for the primal problem, one of which is primal feasible, and use it to identify a most violated constraint; at the same time this gives a lower bound to the objective value of the original problem. This violated constraint is added to the relaxed problem which is solved again, which gives a new upper bound etc. We present computational experience indicating that time savings of 50–80% over the simplex method can be obtained by this method, which we call PAPA, the Pivot and Probe Algorithm. This report was prepared as part of the activities of the Management Science Research Group, Carnegie-Mellon University, under Contract No. N00014-75-C-0621 NR 047-048 with the U.S. Office of Naval Research. Reproduction in whole or part is permitted for any purpose of the U.S. Government.     

An implementation of the simplex method for linear programming problems with variable upper bounds

Special methods for dealing with constraints of the formx _j x _k , called variable upper bounds, were introduced by Schrage. Here we describe a method that circumvents the massive degeneracy inherent in these constraints and show how it can be implemented using triangular basis factorizations.This research was partially supported by National Science Foundation Grant ECS-7921279 and by a Guggenheim fellowship.     

On the average number of steps of the simplex method of linear programming

The goal is to give some theoretical explanation for the efficiency of the simplex method of George Dantzig. Fixing the number of constraints and using Dantzig's self-dual parametric algorithm, we show that the number of pivots required to solve a linear programming problem grows in proportion to the number of variables on the average. Supported in part by NSF Grant #MCS-8102262.     

On the non-polynomiality of the relaxation method for systems of linear inequalities

A class of linear programs is given in which the relaxation method for inequalities, under the same operating rules as Khacian's method, is not polynomial in the length of the input. This result holds for any value of the relaxation parameter.This research was supported in part by the D.G.E.S. (Quebec), the N.S.E.R.C. of Canada under grant A 4152, and the S.S.H.R.C. of Canada.     

A nonlinear equation for linear programming

We present a characterization of the normal optimal solution of the linear program given in canonical form max{c ^tx: Ax = b, x 0}. (P) We show thatx ^* is the optimal solution of (P), of minimal norm, if and only if there exists anR > 0 such that, for eachr R, we havex ^* = (rc – A^t_r)₊. Thus, we can findx ^* by solving the following equation for _r A(rc – A^t_r)₊ = b. Moreover,(1/r) _r then converges to a solution of the dual program.On leave from The University of Alberta, Edmonton, Canada. Research partially supported by the National Science and Engineering Research Council of Canada.     

Optimality in transient markov chains and linear programming

It is shown how a discrete Markov programming problem can be transformed, using a linear program, into an equivalent problem from which the optimal decision rule can be trivially deduced. This transformation is applied to problems which have either transient probabilities or discounted costs.This research was supported by the National Research Council of Canada, Grant A7751.     

An advanced implementation of the Dantzig—Wolfe decomposition algorithm for linear programming

Since the original work of Dantzig and Wolfe in 1960, the idea of decomposition has persisted as an attractive approach to large-scale linear programming. However, empirical experience reported in the literature over the years has not been encouraging enough to stimulate practical application. Recent experiments indicate that much improvement is possible through advanced implementations and careful selection of computational strategies. This paper describes such an effort based on state-of-the-art, modular linear programming software (IBM's MPSX/370).     

An inexact approach to solving linear semi-infinite programming problems

In this paper, we propose an “inexact solution” approach to deal with linear semi-infinite programming problems with finitely many variables and infinitely many constraints over a compact metric space. A general convergence proof with some numerical examples are given and the advantages of using this approach are discussed     

An LP-based algorithm for the correction of inconsistent linear equation and inequality systems

An algorithm based on linear programming is proposed, which finds for an inconsistent system minimal corrections of the matrix and RHS vector among those providing its consistency     

A simplex algorithm for piecewise-linear programming I: Derivation and proof

The simplex method for linear programming can be extended to permit the minimization of any convex separable piecewise-linear objective, subject to linear constraints. This three-part paper develops and analyzes a general, computationally practical simplex algorithm for piecewiselinear programming.Part I derives and justifies the essential steps of the algorithm, by extension from the simplex method for linear programming in bounded variables. The proof employs familiar finite-termination arguments and established piecewise-linear duality theory.Part II considers the relaxation of technical assumptions pertaining to finiteness, feasibility and nondegeneracy of piecewise-linear programs. Degeneracy is found to have broader consequences than in the linear case, and the standard techniques for prevention of cycling are extended accordingly.Part III analyzes the computational requirements of piecewise-linear programming. The direct approach embodied in the piecewise-linear simplex algorithm is shown to be inherently more efficient than indirect approaches that rely on transformation of piecewise-linear programs to equivalent linear programs. A concluding section surveys the many applications of piecewise-linear programming in linear programming,l ₁ estimation, goal programming, interval programming, and nonlinear optimization.This research has been supported in part by the National Science Foundation under grant MCS-8217261.     

Generalized capacities in linear programming

Within the framework of linear programming in paired spaces (Duffin, Kretschmer) we introduce quantities which are analogs of direct and adjoint capacity in potential theory (Ohtsuka), and we give conditions for these quantities to be equal