sparseHessianFD: Interface to ACM TOMS Algorithm 636, for computing sparse Hessians

Computes Hessian of a scalar-valued function, and returns it in sparse Matrix format. The user must supply the objective function, the gradient, and the row and column indices of the non-zero elements of the lower triangle of the Hessian (i.e., the sparsity structure must be known in advance). The algorithm exploits this sparsity, so Hessians can be computed quickly even when the number of arguments to the objective functions is large. This package is intended to be useful when optimizing objective functions using optimizers than can exploit this sparsity, such as the trustOptim package. The underlying algorithm is ACM TOMS Algorithm 636, written by Coleman, Garbow and More (ACM Transactions on Mathematical Software, 11:4, Dec. 1985). The package also includes functions to sample from, and compute the log density of, a multivariate normal distribution when the precision matrix is sparse.

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