Gale duality, decoupling, parameter homotopies, and monodromy

2014 Spring.Numerical Algebraic Geometry (NAG) has recently seen significantly increased application among scientists and mathematicians as a tool that can be used to solve nonlinear systems of equations, particularly polynomial systems. With the many recent advances in the field, we can now routinely solve problems that could not have been solved even 10 years ago. We will give an introduction and overview of numerical algebraic geometry and homotopy continuation methods; discuss heuristics for preconditioning fewnomial systems, as well as provide a hybrid symbolic-numerical algorithm for computing the solutions of these types of polynomials and associated software called galeDuality; describe a software module of bertini named paramotopy that is scientific software specifically designed for large-scale parameter homotopy runs; give two examples that are parametric polynomial systems on which the aforementioned software is used; and finally describe two novel algorithms, decoupling and a heuristic that makes use of monodromy

APPROXIMATE GROBNER BASES A BACKWARDS APPROACH

The main result of this thesis is to give a method for approximating the Grobner basis of an approximate polynomial system. The Grobner basis of a polynomial system is arguably the most fundamental object of exact computation polynomial algebra, as it answers many of the important questions of commutative algebra, such as ideal membership and computation of the Hilbert polynomial. It is traditionally computed using variants of Buchberger’s algorithm. Here, we take a backwards approach, and show that a Grobner basis can be computed using the Hilbert polynomial and another important basis from the jet theory of partial differential equations: an involutive basis. This direction, motivated by approximate systems, will allow us to avoid the strict monomial orderings and ordered elimination (reduction) strategies, at the heart of Buchberger-type methods, which are usually numerically unstable. For the the computation of exact bases for an ideal near to the one from which we began, we make avid use of structured (numerical) linear algebra. Additionally, we introduce approximate leading terms and an approximate reduced row echelon form. Neither of these require Gaussian elimination, unlike the exact case

Application of Numerical Algebraic Geometry and Numerical Linear Algebra to PDE

The computational difficulty of completing nonlinear pde to involutive form by differential elimination algorithms is a significant obstacle in applications. We apply numerical methods to this problem which, unlike existing symbolic methods for exact systems, can be applied to approximate systems arising in applications. We use Numerical Algebraic Geometry to process the lower order leading nonlinear parts of such pde systems. The irreducible components of such systems are represented by certain generic points lying on each component and are computed by numerically following paths from exactly given points on components of a related system. To check the conditions for involutivity Numerical Linear Algebra techniques are applied to constant matrices which are the leading linear parts of such systems evaluated at the generic points. Representations for the constraints result from applying a method based on Polynomial Matrix Theory. Examples to illustrate the new approach are given. The scope of the method, which applies to complexified problems, is discussed. Approximate ideal and differential ideal membership testing are also discussed