Package {Uno}

Type: Package
Title: R Interface to the 'Uno' Nonlinear Optimization Solver
Version: 2.7.3
Description: Bindings to 'Uno' (Unifying Nonlinear Optimization), a C++ solver for smooth nonlinearly constrained optimization. 'Uno' unifies Lagrange-Newton methods, including sequential quadratic programming and interior-point methods, by decomposing them into interacting building blocks (constraint-relaxation, inequality-handling, Hessian, and globalization strategies) that can be freely combined, either through options or through presets that reproduce established solvers such as 'filterSQP' and 'IPOPT'. The framework is described in Vanaret and Leyffer (2024) <doi:10.48550/arXiv.2406.13454>.
License: MIT + file LICENSE
Copyright: file inst/COPYRIGHTS
URL: https://bnaras.github.io/Uno/, https://github.com/bnaras/Uno
BugReports: https://github.com/bnaras/Uno/issues
SystemRequirements: CMake (>= 3.16), C++17, GNU make
Imports: rmumps (≥ 5.2.1-41)
LinkingTo: cpp11, rmumps
Suggests: cpp11, tinytest, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
RoxygenNote: 7.3.3
Encoding: UTF-8
Packaged: 2026-06-03 04:37:30 UTC; naras
Author: Balasubramanian Narasimhan [aut, cre], Charlie Vanaret [aut, cph] (Designed and implemented Uno), Sven Leyffer [aut, cph] (Co-developed the Uno framework), HiGHS development team [cph] (Bundled HiGHS LP/QP/MIP solver (MIT); see inst/COPYRIGHTS)
Maintainer: Balasubramanian Narasimhan <naras@stanford.edu>
Repository: CRAN
Date/Publication: 2026-06-08 19:20:02 UTC

Uno: R interface to the Uno nonlinear optimization solver

Description

R bindings to Uno (Unifying Nonlinear Optimization), a C++ solver for nonlinearly constrained optimization, via Uno's C API. The HiGHS QP/LP subproblem solver is built from source for the SQP presets; the MUMPS linear solver used by the interior-point preset is reached at runtime through the rmumps package. Intended as a nonlinear (DNLP) backend for CVXR; this is the R analog of the unopy Python package.

Author(s)

Maintainer: Balasubramanian Narasimhan naras@stanford.edu

Authors:

See Also

uno_solve to solve a nonlinear program, uno_version for the linked Uno version.

Solve a nonlinear program with Uno

Description

Solve a nonlinear program with Uno

Usage

uno_solve(
  n,
  lb,
  ub,
  sense,
  obj,
  grad,
  m,
  cl,
  cu,
  cons,
  jac_rows,
  jac_cols,
  jac,
  hess_rows,
  hess_cols,
  hess,
  x0,
  preset,
  base_indexing,
  verbose,
  options = list(),
  lagrangian_sign = c("negative", "positive"),
  dual0 = NULL,
  iter_callback = NULL,
  log_callback = NULL
)

Arguments

n

number of variables.

lb, ub

variable lower/upper bounds (length 'n'; use '-Inf'/'Inf').

sense

'"minimize"' or '"maximize"'.

obj, grad

objective 'function(x)' and its gradient 'function(x)'.

m

number of constraints (0 for unconstrained).

cl, cu

constraint lower/upper bounds (length 'm').

cons

constraint 'function(x)' returning a length-'m' vector.

jac_rows, jac_cols

COO row/column indices of the Jacobian nonzeros.

jac

Jacobian 'function(x)' returning the nonzero values.

hess_rows, hess_cols

COO indices of the lower-triangular Hessian.

hess

Lagrangian Hessian 'function(x, sigma, lambda)' returning the lower-triangular nonzero values, or 'NULL' (Uno then uses an L-BFGS approximation, which the HiGHS subproblem solver cannot use).

x0

initial primal iterate (length 'n').

preset

Uno preset, e.g. '"filtersqp"' (SQP) or '"ipopt"' (interior point, using MUMPS as the linear solver).

base_indexing

0 for C-style or 1 for Fortran-style COO indices.

verbose

if ‘FALSE', suppress Uno’s solution printout.

options

a named list of Uno solver options applied AFTER the preset (so they override it), e.g. 'list(max_iterations = 200L, tolerance = 1e-8, linear_solver = "MUMPS")‘. Each value is coerced to the option’s declared Uno type; an unknown option name or an unacceptable value raises an error.

lagrangian_sign

the Lagrangian multiplier sign convention the 'hess' callback uses: '"positive"' for L = \sigma f + y^\top c (the standard convention used by IPOPT and the sparsediff oracle) or '"negative"' for L = \sigma f - y^\top c. Defaults to ‘"negative"', matching Uno’s own C-API default. **Must match the convention your 'hess' returns**, otherwise the Lagrangian Hessian's constraint terms get the wrong sign (invisible when all constraints are linear, since their Hessian is zero).

dual0

optional warm-start dual iterate, or ‘NULL' (Uno’s default).

iter_callback

optional 'function(info)' called at each acceptable iterate; return 'TRUE' to terminate the solve early. 'info' is a named list with 'primals', 'lower_bound_dual', 'upper_bound_dual', 'constraint_dual', 'objective_multiplier', and the 'primal_feasibility'/'stationarity'/ 'complementarity' residuals. Errors in the callback are caught and treated as "do not terminate". 'NULL' disables it.

log_callback

optional ‘function(text)' that receives Uno’s output stream in chunks (a sink for the solver log); 'NULL' leaves output on stdout. Independent of 'verbose' (which controls how much Uno prints).

Value

a named list. The 'optimization_status' and 'solution_status' are **named integers** of the form ‘c(SUCCESS = 0L)': the value is Uno’s enum code and the name is its canonical label, so you can key a status map by 'names(status)' and still read the code (e.g. 'status[[1L]]'). The list also holds the objective, primal and dual solutions ('constraint_dual', 'lower_bound_dual', 'upper_bound_dual'), KKT residuals, and per-callback evaluation counters.

Linked Uno version

Description

Returns the version string of the Uno C++ library this package is built against.

Value

A character scalar, e.g. "2.7.2".

Examples

uno_version()