HMSNC2015
副标题：
Hybrid symbolic-numeric computation methods, which first appeared some twenty years ago, have gained considerable prominence. Algorithms have been developed that improve numeric robustness (e.g., in quadrature or solving ODE systems) using symbolic techniques prior to, or during, a numerical solution. Likewise, traditionally symbolic algorithms have seen speed improvements from adaptation of numeric methods (e.g., lattice reduction methods). There is also an emerging approach of characterizing, locating, and solving ``interesting nearby problems'', wherein one seeks an important event (for example a nontrivial factorization or other useful singularities), that in some measure is close to a given problem (one that might have only imprecisely specified data). Many novel techniques have been developed in these complementary areas, but there is a general belief that a deeper understanding and wider approach will foster future progress.
The problems we are interested are driven by applications in computational physics (quadrature of singular integrals), dynamics (symplectic integrators), robotics (global solutions of direct and inverse problems near singular manifolds), control theory (stability of models), and the engineering of large-scale continuous and hybrid discrete-continuous dynamical systems. Emphasis will be given to validated and certified outputs via algebraic and exact techniques, error estimation, interval techniques and optimization strategies.
This workshop will follow up on the exciting
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