Numerical recipes: the art of scientific computing
Numerical recipes: the art of scientific computing
Maximum Likelihood Estimation Using Parallel Computing: An Introduction to MPI
Computational Economics
A Parallel Implementation of the Simplex Function Minimization Routine
Computational Economics
Multi-core CPUs, Clusters, and Grid Computing: A Tutorial
Computational Economics
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Many economic models are completed by finding a parameter vector 驴 that optimizes a function f(驴), a task that can only be accomplished by iterating from a starting vector 驴0. Use of a generic iterative optimizer to carry out this task can waste enormous amounts of computation when applied to a class of problems defined here as finite mixture models. The finite mixture class is large and important in economics and eliminating wasted computations requires only limited changes to standard code. Further, the approach described here greatly increases gains from parallel execution and opens possibilities for re-writing objective functions to make further efficiency gains.