Gun Srijuntongsiri

Gun Srijuntongsiri, Stephen A. Vavasis

This article considers the problem of solving a system of $n$ real polynomial equations in $n+1$ variables. We propose an algorithm based on Newton's method and subdivision for this problem. Our algorithm is intended only for nondegenerate cases, in which case the solution is a 1-dimensional curve. Our first main contribution is a definition of a condition number measuring reciprocal distance to degeneracy that can distinguish poor and well conditioned instances of this problem. (Degenerate problems would be infinitely ill conditioned in our framework.) Our second contribution, which is the main novelty of our algorithm, is an analysis showing that its running time is bounded in terms of the condition number of the problem instance as well as $n$ and the polynomial degrees.

Gun Srijuntongsiri, Stephen A. Vavasis

In [5], Srijuntongsiri and Vavasis propose the "Kantorovich-Test Subdivision algorithm", or KTS, which is an algorithm for finding all zeros of a polynomial system in a bounded region of the plane. This algorithm can be used to find the intersections between a line and a surface. The main features of KTS are that it can operate on polynomials represented in any basis that satisfies certain conditions and that its efficiency has an upper bound that depends only on the conditioning of the problem and the choice of the basis representing the polynomial system. This article explores in detail the dependence of the efficiency of the KTS algorithm on the choice of basis. Three bases are considered: the power, the Bernstein, and the Chebyshev bases. These three bases satisfy the basis properties required by KTS. Theoretically, Chebyshev case has the smallest upper bound on its running time. The computational results, however, do not show that Chebyshev case performs better than the other two.

Duangpen Jetpipattanapong, Gun Srijuntongsiri

Quadratic programmingis a class of constrained optimization problem with quadratic objective functions and linear constraints. It has applications in many areas and is also used to solve nonlinear optimization problems. This article focuses on the equality constrained quadratic programs whose constraint matrices are block diagonal. Using the direct solution method, we propose a new pivot selection algorithm for the factorization of the Karush-Kuhn-Tucker(KKT) matrix for this problem that maintains the sparsity and stability of the problem. Our experiments show that our pivot selection algorithm appears to produce no fill-ins in the factorizationof such matrices. In addition, we compare our method with MA57 and find that the factors produced by our algorithm are sparser.

Duangpen Jetpipattanapong, Gun Srijuntongsiri

We propose a new pivot selection technique for symmetric indefinite factorization of sparse matrices. Such factorization should maintain both sparsity and numerical stability of the factors, both of which depend solely on the choices of the pivots. Our method is based on the minimum degree algorithm and also considers the stability of the factors at the same time. Our experiments show that our method produces factors that are sparser than the factors computed by MA57 and are stable.