TheD 2-triangulation for simplicial homotopy algorithms for computing solutions of nonlinear equations

A triangulation of arbitrary refinement of grid sizes of (0, 1] × ⁿ is proposed for simplicial homotopy algorithms for computing solutions of nonlinear equations. On each level the new triangulation, called theD ₂-triangulation, subdivides ⁿ into simplices according to theD ₁-triangulation. We prove that theD ₂-triangulation is superior to theK ₂-triangulation andJ ₂-triangulation in the number of simplices. Numerical tests show that the simplicial homotopy algorithm based on theD ₂-triangulation indeed is much more efficient.     

Interior path following primal-dual algorithms. part I: Linear programming

We describe a primal-dual interior point algorithm for linear programming problems which requires a total of number of iterations, whereL is the input size. Each iteration updates a penalty parameter and finds the Newton direction associated with the Karush-Kuhn-Tucker system of equations which characterizes a solution of the logarithmic barrier function problem. The algorithm is based on the path following idea.     

An O(n 3 L) adaptive path following algorithm for a linear complementarity problem

In this paper we propose an O(n ³ L) algorithm which is a modification of the path following algorithm [8] for a linear complementarity problem. The path following algorithm has to take a short step size in each iteration in order to bound the number of overall arithmetic operations by O(n ³ L). In practical computation, we can determine the step size adaptively. Mizuno, Yoshise, and Kikuchi [11] reported that such an adaptive algorithm required about O(L) iterations for some test problems. Here we show that we can use a rank one update technique in the adaptive algorithm so that the number of overall arithmetic operations is theoretically bounded by O(n ³ L).Research supported in part by the U.S. Army Research Office through the Mathematical Sciences Institute of Cornell University.Research supported in part by NSF grants ECS-8602534 and DMS-8904406 and ONR contract N-00014-87-K0212.     

On the worst case complexity of potential reduction algorithms for linear programming

There are several classes of interior point algorithms that solve linear programming problems in O(√nL) iterations. Among them, several potential reduction algorithms combine both theoretical (O(√nL) iterations) and practical efficiency as they allow the flexibility of line searches in the potential function, and thus can lead to practical implementations. It is a significant open question whether interior point algorithms can lead to better complexity bounds. In the present paper we give some negative answers to this question for the class of potential reduction algorithms. We show that, even if we allow line searches in the potential function, and even for problems that have network structure, the bound O(√nL) is tight for several potential reduction algorithms, i.e., there is a class of examples with network structure, in which the algorithms need at least Θ(√nL) iterations to find an optimal solution. The research of the author was partially supported by a Presidential Young Investigator Award DDM-9158118 with matching funds from Draper Laboratory.     

A survey of average case complexity for linear multivariate problems

We survey recent results on the average case complexity for linear multivariate problems. Our emphasis is on problems defined on spaces of functions of d variables with large d. We present the sharp order of the average case complexity for a number of linear multivariate problems as well as necessary and sufficient conditions for the average case complexity not to be exponential in d. Dedicated to the 50th anniversary of the journal. The text was submitted by the authors in English.