Prospects for Simulated Annealing Algorithms in Automatic Differentiation
SAGA '01 Proceedings of the International Symposium on Stochastic Algorithms: Foundations and Applications
Computing sparse Hessians with automatic differentiation
ACM Transactions on Mathematical Software (TOMS)
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Jacobian matrices can be accumulated by applying the chain rule to vector functions given as computer programs in different orders resulting in varying operations counts while yielding identical results, up to round-off. The minimization of the number of operations performed leads to a computationally hard combinatorial optimization problem based on vertex elimination in computational graphs. This paper discusses simulated annealing as a method for generating nearly optimal Jacobian code.