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sparsediff sparsediff logo

Lifecycle: experimental

sparsediff is a thin R interface to the SparseDiffEngine C library — the sparse Jacobian and Hessian differentiation backend used by CVXPY for its Disciplined Nonlinear Programming (DNLP) extension. It is the R analog of the Python sparsediffpy package and wraps the same C library.

You build a nonlinear expression graph from the sd_* atom constructors, assemble it into a problem, and evaluate — at any primal point — the objective value, its gradient, the sparse constraint Jacobian (COO), and the lower-triangular Lagrangian Hessian (COO).

This is a low-level backend. The intended user is a higher-level modelling layer such as CVXR, not someone writing models by hand.

Installation

# install.packages("remotes")
remotes::install_github("bnaras/sparsediff")

The SparseDiffEngine C sources are bundled and built with R’s own toolchain and BLAS; there is nothing external to install.

Quick example

f(x) = sum(exp(x)) with one linear constraint g(x) = sum(x):

library(sparsediff)

n   <- 3L
x   <- sd_variable(d1 = n, d2 = 1L, var_id = 0L, n_vars = n)
obj <- sd_sum(sd_exp(x), axis = -1L)        # sum(exp(x))
g1  <- sd_sum(x, axis = -1L)                # sum(x)

prob <- sd_problem(obj, constraints = list(g1), verbose = FALSE)
sd_init_derivatives(prob)
sd_init_jacobian_coo(prob)
sd_init_hessian_coo(prob)

u <- c(0, 0.5, 1)
sd_objective_forward(prob, u)               # 5.367003
sd_gradient(prob)                           # exp(u): 1.000 1.649 2.718
sd_jacobian_values(prob)                    # d/dx sum(x): 1 1 1
sd_hessian_values(prob, obj_w = 1, w = 0)   # diag(exp(u))

See vignette("sparsediff") for the full walk-through, including parameters and fast re-evaluation.

License

Apache License 2.0. The bundled SparseDiffEngine is by Daniel Cederberg and William Zijie Zhang.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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