docs: add R interface README — Uno R package now on CRAN

docs/interfaces/r.md

@@ -0,0 +1,60 @@
+# Uno (R package)
+
+[Uno](https://CRAN.R-project.org/package=Uno) is an R wrapper for Uno, available on CRAN. It provides R bindings to Uno's C API: you describe a nonlinear program with R callbacks for the objective, gradient, constraints, Jacobian and Lagrangian Hessian, and Uno solves it. It is the R analog of the [`unopy`](python.md) Python interface.
+
+This R interface is developed and maintained by [Balasubramanian Narasimhan](https://github.com/bnaras). Sources, documentation and issue tracker live in a separate repository: [github.com/bnaras/Uno](https://github.com/bnaras/Uno) (package website: [bnaras.github.io/Uno](https://bnaras.github.io/Uno/)).
+
+## Installation
+
+`Uno` is a registered CRAN package and can be installed with:
+
+```r
+install.packages("Uno")
+```
+
+The development version can be installed from GitHub:
+
+```r
+# install.packages("remotes")
+remotes::install_github("bnaras/Uno")
+```
+
+A C++17 compiler and CMake (>= 3.16) are required for source builds: the package builds Uno and the HiGHS QP/LP subproblem solver from source. The MUMPS linear solver used by the interior-point preset is reached at run time through the [rmumps](https://CRAN.R-project.org/package=rmumps) package, so no separate MUMPS installation is needed.
+
+## Getting started
+
+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.
+
+```r
+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 2
+```
+
+Any Uno solver option can be passed through `options` as a named list (applied
+after the preset). See `vignette("Uno")` and the [package website](https://bnaras.github.io/Uno/) for the full walk-through.
+
+If you encounter any issues with the R interface, please [open an issue](https://github.com/bnaras/Uno/issues) on the package repository.

interfaces/R/README.md

@@ -0,0 +1,60 @@
+# Uno (R package)
+
+[Uno](https://CRAN.R-project.org/package=Uno) is an R wrapper for Uno, available on CRAN. It provides R bindings to Uno's C API: you describe a nonlinear program with R callbacks for the objective, gradient, constraints, Jacobian and Lagrangian Hessian, and Uno solves it. It is the R analog of the [`unopy`](../Python) Python interface.
+
+The R interface is developed and maintained by [Balasubramanian Narasimhan](https://github.com/bnaras). Sources, documentation and issue tracker live in a separate repository: [github.com/bnaras/Uno](https://github.com/bnaras/Uno) (package website: [bnaras.github.io/Uno](https://bnaras.github.io/Uno/)).
+
+## Installation
+
+`Uno` is a registered CRAN package and can be installed with:
+
+```r
+install.packages("Uno")
+```
+
+The development version can be installed from GitHub:
+
+```r
+# install.packages("remotes")
+remotes::install_github("bnaras/Uno")
+```
+
+A C++17 compiler and CMake (>= 3.16) are required for source builds: the package builds Uno and the HiGHS QP/LP subproblem solver from source. The MUMPS linear solver used by the interior-point preset is reached at run time through the [rmumps](https://CRAN.R-project.org/package=rmumps) package, so no separate MUMPS installation is needed.
+
+## Getting started
+
+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.
+
+```r
+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 2
+```
+
+Any Uno solver option can be passed through `options` as a named list (applied
+after the preset). See `vignette("Uno")` and the [package website](https://bnaras.github.io/Uno/) for the full walk-through.
+
+If you encounter any issues with the R interface, please [open an issue](https://github.com/bnaras/Uno/issues) on the package repository.

mkdocs.yml

@@ -24,6 +24,7 @@ nav:
   - 'Interfaces':
     - 'Python': 'interfaces/python.md'
     - 'Julia': 'interfaces/julia.md'
+    - 'R': 'interfaces/r.md'
     - 'C': 'interfaces/c.md'
     - 'AMPL': 'interfaces/ampl.md'
     - 'Fortran': 'interfaces/fortran.md'