Computer algebra: symbolic and algebraic computation (2nd ed.)
Loop detection in surface patch intersections
Computer Aided Geometric Design
Computer Aided Geometric Design
Ray tracing trimmed rational surface patches
SIGGRAPH '90 Proceedings of the 17th annual conference on Computer graphics and interactive techniques
Algorithms for intersecting parametric and algebraic curves I: simple intersections
ACM Transactions on Graphics (TOG)
Geometric constraint solver using multivariate rational spline functions
Proceedings of the sixth ACM symposium on Solid modeling and applications
Shape Interrogation for Computer Aided Design and Manufacturing
Shape Interrogation for Computer Aided Design and Manufacturing
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
Real-time GPU rendering of piecewise algebraic surfaces
ACM SIGGRAPH 2006 Papers
Subdivision methods for solving polynomial equations
Journal of Symbolic Computation
Isolating real roots of real polynomials
Proceedings of the 2009 international symposium on Symbolic and algebraic computation
Computation of the solutions of nonlinear polynomial systems
Computer Aided Geometric Design
Optimal bounding cones of vectors in three dimensions
Information Processing Letters
Solving polynomial systems using no-root elimination blending schemes
Computer-Aided Design
Solving polynomial systems using no-root elimination blending schemes
Computer-Aided Design
GPU-based parallel algorithms for sparse nonlinear systems
Journal of Parallel and Distributed Computing
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This paper proposes a parallel solver for the nonlinear systems in Bernstein form based on subdivision and the Newton-Raphson method, where the Kantorovich theorem is employed to identify the existence of a unique root and guarantee the convergence of the Newton-Raphson iterations. Since the Kantorovich theorem accommodates a singular Jacobian at the root, the proposed algorithm performs well in a multiple root case. Moreover, the solver is designed and implemented in parallel on Graphics Processing Unit(GPU) with SIMD architecture; thus, efficiency for solving a large number of systems is improved greatly, an observation validated by our experimental results.