Numerical data structures for positive dimensional solution sets of polynomial systems are sets of generic points cut out by random planes of complementary dimension. We may represent the linear spaces defined by those planes either by explicit linear equations or in parametric form. These descriptions are respectively called extrinsic and intrinsic representations. While intrinsic representations lower the cost of the linear algebra operations, we observe worse condition numbers. In this paper we describe the local adaptation of intrinsic coordinates to improve the numerical conditioning of sampling algebraic sets. Local intrinsic coordinates also lead to a better stepsize control. We illustrate our results with Maple experiments and computations with PHCpack on some benchmark polynomial systems.
2000 Mathematics Subject Classification. Primary 65H10; Secondary 14Q99, 68W30.
Key words and phrases. algebraic sets, condition numbers, generic points, local intrinsic coordinates, numerical algebraic geometry, path tracking, polynomial systems, sampling.