Relaxed variants of Karmarkar's algorithm for linear programs with unknown optimal objective value

Variants of Karmarkar's algorithm are given for solving linear programs with unknown optimal objective valuez ^*. These new methods combine the approach of Goldfarb and Mehrotra for relaxing the requirement that certain projections be computed exactly with the approach of Todd and Burrell for generating an improving sequence of lower bounds forz ^* using dual feasible solutions. These methods retain the polynomial-time complexity of Karmarkar's algorithm.This research was supported in part by NSF Grants DMS-85-12277 and CDR-84-21402, and ONR Contract N0014-87-K0214.     

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.     

A reduced-gradient variant of Karmarkar's algorithm and null-space projections

This paper discusses the relationship between Karmarkar's new method for linear programming and the traditional simplex method. It is shown how null-space Karmarkar projections can be done using a basis matrix to compute the projections in the null space. Preliminary computational evidence shows that problems exist in the choice of a basis matrix, but that, given a basis, very inexact and computationally efficient projections are computationally sound.     

COMPUTING KARMARKAR＇S PROJECTIONS QUICKLY BY USING MATRIX FACTORIZATION

in this paper we compute Karmarkar‘s projections quickly using MoorePenrose g-inverse and matrix factorization. So the computation work of (A^T D^2 A)^-1 is decreased.     

On the convexity of the multiplicative version of Karmarkar's potential function

Karmarkar's potential function is quasi-convex, but not convex. This note investigates the multiplicative version of the potential function, and shows that it is not necessarily convex in general, but is strictly convex when the corresponding feasible region is bounded. This implies that the multiplicative version of the potential function in Karmarkar's algorithm is convex, since it works on a simplex.     

Conical projection algorithms for linear programming

The Linear Programming Problem is manipulated to be stated as a Non-Linear Programming Problem in which Karmarkar's logarithmic potential function is minimized in the positive cone generated by the original feasible set. The resulting problem is then solved by a master algorithm that iteratively rescales the problem and calls an internal unconstrained non-linear programming algorithm. Several different procedures for the internal algorithm are proposed, giving priority either to the reduction of the potential function or of the actual cost. We show that Karmarkar's algorithm is equivalent to this method in the special case in which the internal algorithm is reduced to a single steepest descent iteration. All variants of the new algorithm have the same complexity as Karmarkar's method, but the amount of computation is reduced by the fact that only one projection matrix must be calculated for each call of the internal algorithm.Research partly sponsored by CNPq-Brazilian National Council for Scientific and Technological Development, by National Science Foundation grant ECS-857362, Office of Naval Research contract N00014-86-K-0295, and AFOSR grant 86-0116.On leave from COPPE-Federal University of Rio de Janeiro, Cx. Postal 68511, 21941 Rio de Janeiro, RJ, Brasil.     

Recovering optimal dual solutions in Karmarkar's polynomial algorithm for linear programming

This paper presents a “standard form” variant of Karmarkar's algorithm for linear programming. The tecniques of using duality and cutting objective are combined in this variant to maintain polynomial-time complexity and to bypass the difficulties found in Karmarkar's original algorithm. The variant works with problems in standard form and simultaneously generates sequences of primal and dual feasible solutions whose objective function values converge to the unknown optimal value. Some computational results are also reported.     

解线性规划问题的梯度投影法

本文叙述了一个求解线性规划问题的梯度投影法,导出了投影矩阵的递推公式,利用此公式可大大减少每次迭代所需的计算量。实例计算表明,本文给出的算法是一有效的算法,在某些方面它要优于Karmarkar算法和单纯形法。     

An interior point algorithm for semi-infinite linear programming

We consider the generalization of a variant of Karmarkar's algorithm to semi-infinite programming. The extension of interior point methods to infinite-dimensional linear programming is discussed and an algorithm is derived. An implementation of the algorithm for a class of semi-infinite linear programs is described and the results of a number of test problems are given. We pay particular attention to the problem of Chebyshev approximation. Some further results are given for an implementation of the algorithm applied to a discretization of the semi-infinite linear program, and a convergence proof is given in this case.     

A BRANCH BOUND METHOD FOR SUBSET SUM PROBLEM

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Updating lower bounds when using Karmarkar's projective algorithm for linear programming

We give two results related to Gonzaga's recent paper showing that lower bounds derived from the Todd-Burrell update can be obtained by solving a one-variable linear programming problem involving the centering direction and the affine direction. We show how these results may be used to update the primal solution when using the dual affine variant of Karmarkar's algorithm. This leads to a dual projective algorithm.This research was partially supported by ONR Grant Number N00014-90-J-1714.     

Linearly constrained convex programming as unconstrained differentiable concave programming

Recently, Fang proposed approximating a linear program in Karmarkar's standard form by adding an entropic barrier function to the objective function and using a certain geometric inequality to transform the resulting problem into an unconstrained differentiable concave program. We show that, by using standard duality theory for convex programming, the results of Fang and his coworkers can be strengthened and extended to linearly constrained convex programs and more general barrier functions.This research was supported by the National Science Foundation, Grant No. CCR-91-03804.     

On Anstreicher's combined phase I—phase II projective algorithm for linear programming

Anstreicher has proposed a variant of Karmarkar's projective algorithm that handles standard-form linear programming problems nicely. We suggest modifications to his method that we suspect will lead to better search directions and a more useful algorithm. Much of the analysis depends on a two-constraint linear programming problem that is a relaxation of the scaled original problem.Research supported in part by NSF Grant ECS-8602534 and ONR Contract N00014-87-K-0212.     

Computing Karmarkar projections quickly

The paper presents a numerical method for computing the projections for Karmarkar's new algorithm for linear programming. The method is simple to implement, fully exploits sparsity, and appears in limited experimentation to have good stability properties. Preliminary numerical experience indicates that the method promises advantages over methods that refactor a matrix at every iteration.Research sponsored by the Air Force Office of Scientific Research, Air Force Systems Command, USAF under Grant AFOSR-86-0170. The US Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon.This work was initiated while the author was at the Graduate School of Administration, University of California, Davis, Davis, California.     

A path-following version of the Todd-Burrell procedure for linear programming

We propose a path-following version of the Todd-Burrell procedure to solve linear programming problems with an unknown optimal value. The path-following scheme is not restricted to Karmarkar's primal step; it can also be implemented with a dual Newton step or with a primal-dual step.This work has been completed with the support from the Fonds National Suisse de la Recherche Scientifique, grant 12-34002.92.     

Stabilization of Interior-Point Methods for Linear Programming

The paper studies numerical stability problems arising in the application of interior-point methods to primal degenerate linear programs. A stabilization procedure based on Gaussian elimination is proposed and it is shown that it stabilizes all path following methods, original and modified Dikin's method, Karmarkar's method, etc.     

A polynomial-time algorithm,based on Newton's method,for linear programming

A new interior method for linear programming is presented and a polynomial time bound for it is proven. The proof is substantially different from those given for the ellipsoid algorithm and for Karmarkar's algorithm. Also, the algorithm is conceptually simpler than either of those algorithms.This research was supported by an NSF Mathematical Sciences Postdoctoral Research Fellowship and by NSF Grant 8120790. The research was performed at the Mathematical Sciences Research Institute in Berkeley, California.     

Karmarkar's algorithm with improved steps

We define a function of the interior of the feasible region of Karmarkar's canonical linear program with the property that its supremum is bounded above by one, if and only if the optimal value of the linear program is zero. We use this function to obtain data-dependent steps for Karmarkar's algorithm. The corresponding reduction in the potential function improves upon the data-independent reduction derived by Padberg. We analyze the general step and consider its potential practical advantages.     

Asymptotic behaviour of Karmarkar's method for linear programming

The asymptotic behaviour of Karmarkar's method is studied and an estimate of the rate of the objective function value decrease is given. Two possible sources of numerical instability are discussed and a stabilizing procedure is proposed.Research supported in part by Republicka zajednica za nauku SR Srbije.     

A unified view of interior point methods for linear programming

The paper shows how various interior point methods for linear programming may all be derived from logarithmic barrier methods. These methods include primal and dual projective methods, affine methods, and methods based on the method of centers. In particular, the paper demonstrates that Karmarkar's algorithm is equivalent to a classical logarithmic barrier method applied to a problem in standard form.Invited paper presented at the Workshop on Supercomputers in Optimization, Minneapolis, Minn., May 1988.The work of this author was supported by the Air Force Office of Scientific Research, Air Force Systems Command, USA, under Grants AFOSR-87-0215 and AFOSR-85-0271. The US Government is authorized to reproduce and distribute reprints for Governmental purposes not withstanding any copyright notation thereon.