Results 1 
4 of
4
A simplex algorithm whose average number of steps is bounded between two quadratic functions of the smaller dimension
 JOURNAL OF THE ACM
, 1985
"... It has been a challenge for mathematicians to confirm theoretically the extremely good performance of simplextype algorithms for linear programming. In this paper the average number of steps performed by a simplex algorithm, the socalled selfdual method, is analyzed. The algorithm is not started ..."
Abstract

Cited by 30 (2 self)
 Add to MetaCart
It has been a challenge for mathematicians to confirm theoretically the extremely good performance of simplextype algorithms for linear programming. In this paper the average number of steps performed by a simplex algorithm, the socalled selfdual method, is analyzed. The algorithm is not started at the traditional point (1,..., but points of the form (1, e, e2,...)T, with t sufficiently small, are used. The result is better, in two respects, than those of the previous analyses. First, it is shown that the expected number of steps is bounded between two quadratic functions cl(min(m, n))' and cz(min(m, n)) ' of the smaller dimension of the problem. This should be compared with the previous two major results in the field. Borgwardt proves an upper bound of 0(n4m1'(n1') under a model that implies that the zero vector satisfies all the constraints, and also the algorithm under his consideration solves only problems from that particular subclass. Smale analyzes the selfdual algorithm starting at (1,..., He shows that for any fixed m there is a constant c(m) such the expected number of steps is less than ~(m)(lnn)"'("+~); Megiddo has shown that, under Smale's model, an upper bound C(m) exists. Thus, for the first time, a polynomial upper bound with no restrictions (except for nondegeneracy) on the problem is proved, and, for the first time, a nontrivial lower bound of precisely the same order of magnitude is established. Both Borgwardt and Smale require the input vectors to be drawn from
An Optimal Algorithm for Realizing a Delaunay
"... Abstract Dillencourt [7] gives a constructive proof for the realizability as a Delaunay triangulation of any triangulation of the interior of a simple polygon. A naive implementation of the construction will take O(n 2) time. I give a simple O(n) algorithm for this problem. An application of this al ..."
Abstract
 Add to MetaCart
Abstract Dillencourt [7] gives a constructive proof for the realizability as a Delaunay triangulation of any triangulation of the interior of a simple polygon. A naive implementation of the construction will take O(n 2) time. I give a simple O(n) algorithm for this problem. An application of this algorithm is generating test data for algorithms that process convex polygons.
THE EXPECTED NUMBER OF EXTREME POINTS OF A RANDOM LINEAR PROGRAM
, 1986
"... There has been increasing attention recently on average case algorithmic performance measures since worst case measures can be qualitatively quite different. An important characteristic of a linear program, relating to Simplex Method performance, is the number of vertices of the feasible region. We ..."
Abstract
 Add to MetaCart
There has been increasing attention recently on average case algorithmic performance measures since worst case measures can be qualitatively quite different. An important characteristic of a linear program, relating to Simplex Method performance, is the number of vertices of the feasible region. We show 2 ~ to be an upper bound on the mean number of extreme points of a randomly generated feasible region with arbitrary probability distributions on the constraint matrix and right hand side vector. The only assumption made is that inequality directions are chosen independently in accordance with a series of independent fair coin tosses.
unknown title
"... Noname manuscript No. (will be inserted by the editor) Intrinsic volumes of symmetric cones and applications in convex programming ..."
Abstract
 Add to MetaCart
Noname manuscript No. (will be inserted by the editor) Intrinsic volumes of symmetric cones and applications in convex programming