We provide R bindings to Uno (Unifying Nonlinear Optimization), a C++ solver for nonlinearly constrained optimization, via Uno’s C API. The interface allows you to describe a nonlinear program with R callbacks for objective, gradient, constraints, Jacobian and Lagrangian Hessian and Uno solves it. This is the R analog of the unopy Python binding, intended as the nonlinear (DNLP) solver backend for CVXR.
Two solver paths are provided:
- the
filtersqpSQP preset, whose QP subproblems are solved by HiGHS (built from source with the package), and - the
ipoptinterior-point preset, whose KKT systems are solved by MUMPS, reached at run time through the rmumps package.
Installation
Install from CRAN as usual or from the repo via:
# install.packages("remotes")
remotes::install_github("bnaras/Uno")A C++17 compiler and CMake (>= 3.16) are required for source builds as the package builds the underlying Uno and HiGHS from source. MUMPS comes from the rmumps dependency, so there is no separate MUMPS installation.
Quick example
Hock–Schittkowski problem 15 (x* = (0.5, 2), f* = 306.5), solved with the interior-point preset. Derivatives are supplied in COO form (0-based indices); the Hessian is the lower triangle of the Lagrangian.
library(Uno)
objective <- function(x) 100 * (x[2] - x[1]^2)^2 + (1 - x[1])^2
gradient <- function(x) c(400 * x[1]^3 - 400 * x[1] * x[2] + 2 * x[1] - 2,
200 * (x[2] - x[1]^2))
constraints <- function(x) c(x[1] * x[2], x[1] + x[2]^2)
jacobian <- function(x) c(x[2], 1, x[1], 2 * x[2])
hessian <- function(x, sigma, lambda)
c(sigma * (1200 * x[1]^2 - 400 * x[2] + 2),
-400 * sigma * x[1] - lambda[1],
200 * sigma - 2 * lambda[2])
res <- uno_solve(
n = 2L, lb = c(-Inf, -Inf), ub = c(0.5, Inf), sense = "minimize",
obj = objective, grad = gradient,
m = 2L, cl = c(1, 0), cu = c(Inf, Inf), cons = constraints,
jac_rows = c(0L, 1L, 0L, 1L), jac_cols = c(0L, 0L, 1L, 1L), jac = jacobian,
hess_rows = c(0L, 1L, 1L), hess_cols = c(0L, 0L, 1L), hess = hessian,
x0 = c(-2, 1), preset = "ipopt", base_indexing = 0L, verbose = FALSE,
options = list(logger = "SILENT")
)
res$objective # 306.5
res$primal # 0.5 2Any Uno solver option can be passed through options as a named list (applied after the preset). See vignette("Uno") for the full walk-through.
Citation
If you use this package, please cite both the R package and the paper describing the Uno solver. Run citation("Uno") for the up-to-date entries, or:
Narasimhan B, Vanaret C, Leyffer S (2026). Uno: R Interface to the Uno Nonlinear Optimization Solver. R package. https://github.com/bnaras/Uno.
Vanaret C, Leyffer S (2024). Implementing a unified solver for nonlinearly constrained optimization. arXiv:2406.13454. https://doi.org/10.48550/arXiv.2406.13454.