Skip to contents

sparsediff: R interface to the SparseDiffEngine differentiation backend

Source: R/sparsediff-package.R
sparsediff-package.Rd

R bindings to the 'SparseDiffEngine' C library — the sparse Jacobian and Hessian differentiation backend used by 'CVXPY' for its Disciplined Nonlinear Programming (DNLP) extension. This package is the R analog of the 'sparsediffpy' Python package and wraps the same C library (pinned at the upstream v0.6.1 release).

Correspondence with 'sparsediffpy'

Every 'sparsediffpy' 0.6.1 function has an sd_* counterpart that calls the same engine routine with the same arguments in the same order. Names map make_<atom> to sd_<atom> and problem_<step> to sd_<step> (for example make_exp to sd_exp, problem_init_jacobian to sd_init_jacobian), with these exceptions:

  • make_multiply: sd_elementwise_mult; make_param_scalar_mult / make_param_vector_mult: sd_scalar_mult / sd_vector_mult.

  • make_left_matmul, make_right_matmul and make_quad_form, which take a format string, are split by format: "sparse" is sd_left_matmul, sd_right_matmul, sd_quad_form; "dense" is sd_left_matmul_dense, sd_right_matmul_dense, sd_quad_form_dense.

  • make_rel_entr: sd_rel_entr, which dispatches on operand size the same way; the two scalar cases are also available directly as sd_rel_entr_first_scalar and sd_rel_entr_second_scalar.

  • get_jacobian_sparsity_coo, problem_eval_jacobian_vals: sd_jacobian_sparsity, sd_jacobian_values; problem_init_hessian_coo_lower_triangular, get_problem_hessian_sparsity_coo, problem_eval_hessian_vals_coo: sd_init_hessian_coo, sd_hessian_sparsity, sd_hessian_values; get_jacobian, get_hessian, get_expr_dimensions, get_expr_size: sd_get_jacobian, sd_get_hessian, sd_get_expr_dimensions, sd_get_expr_size.

  • Arguments that are optional in 'sparsediffpy' are required here (R stubs have no defaults): verbose of sd_problem, values of sd_parameter (the engine requires it anyway), and n_vars of sd_hstack / sd_vstack, which 'sparsediffpy' takes from the first argument.

  • The parameter argument of the sparse matrix products is not exposed: the engine aborts on it.

Indices are 0-based as in 'sparsediffpy'. CSR results are a list with data, indices, indptr and shape, the pieces of its (data, indices, indptr, (m, n)) tuple.

See also

Useful links:

Author

Maintainer: Balasubramanian Narasimhan naras@stanford.edu

Authors:

  • Balasubramanian Narasimhan naras@stanford.edu

  • Daniel Cederberg (Author of the bundled SparseDiffEngine C library) [copyright holder]

  • William Zijie Zhang (Author of the bundled SparseDiffEngine C library) [copyright holder]