7 research outputs found
Properties of polynomial bases used in a line-surface intersection algorithm
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
A Condition Number Analysis of a Line-Surface Intersection Algorithm
We propose an algorithm based on Newton's method and subdivision 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,
which has applications in graphics and computer-aided geometric design. The
algorithm can operate on polynomials represented in any basis that satisfies a
few conditions. The power basis, the Bernstein basis, and the first-kind
Chebyshev basis are among those compatible with the algorithm. The main novelty
of our algorithm is an analysis showing that its running is bounded only in
terms of the condition number of the polynomial's zeros and a constant
depending on the polynomial basis
An adaptive iterative/subdivision hybrid algorithm for curve/curve intersection
The behavior of the iterative/subdivision hybrid algorithm for curve/curve
intersection proposed in [20] depends on the choice of the domain for their
convergence test. Using either too small or too large test domain may cause the
test to fail to detect cases where Newton's method in fact converges to a
solution, which results in unnecessary additional subdivisions and consequently
more computation time. We propose a modification to the algorithm to adaptively
adjust the test domain size according to what happens during the test of the
parent region. This is in contrast to the original algorithm whose test domain
is always a fixed multiple of the input domain under consideration.
Computational results show that the proposed algorithm is slightly more
efficient than the original algorithm
A condition number analysis of an algorithm for solving a system of polynomial equations with one degree of freedom
This article considers the problem of solving a system of real polynomial
equations in 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
and the polynomial degrees
Properties of polynomial bases used in a line-surface intersection algorithm
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
Properties of polynomial bases used in a line-surface intersection algorithm
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
Properties of polynomial bases used in a line-surface intersection algorithm
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