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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:
filtersqp SQP preset, whose QP
subproblems are solved by HiGHS, reached at run time
through the highs
package, andipopt interior-point preset, whose
KKT systems are solved by MUMPS, reached at run time
through the rmumps package
(uno_mumps_version() tells which MUMPS that is).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 from source. HiGHS comes
from the highs dependency (version 1.15.1-2 or later, which
exports HiGHS’ C API to other packages) and MUMPS from the
rmumps dependency, so there is no separate HiGHS or MUMPS
installation and nothing of either is compiled here. A source build
binds to the rmumps installed at the time:
rmumps 5.2.1-44 or later through its versioned C interface
(rmumps 5.9.1-1 brings MUMPS 5.9.1), rmumps
5.2.1-43 through the legacy dmumps_c entry point.
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.
To cite the R package, run citation("Uno"), or:
Narasimhan B (2026). Uno: R Interface to the Uno Nonlinear Optimization Solver. R package. https://github.com/bnaras/Uno.
To cite the Uno solver itself, use the references its authors maintain under “Cite Uno”: the theory paper (Vanaret and Leyffer, 2026, Mathematical Programming Computation, https://doi.org/10.1007/s12532-026-00310-9) and the software paper (Vanaret and Montoison, 2026, Journal of Open Source Software, https://doi.org/10.21105/joss.10229).
MIT. The bundled Uno solver is Copyright Charlie Vanaret and
contributors, also MIT; see inst/COPYRIGHTS for every
copyright holder of bundled code. HiGHS and MUMPS are not bundled; they
come from the highs and rmumps packages.
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.