Constrained Nonlinear Optimization

Augmented Lagrangian Adaptive Barrier Minimization Algorithm for optimizing smooth nonlinear objective functions with constraints. Linear or nonlinear equality and inequality constraints are allowed.

Comparison of Phylogenetic Trees Using Quartet and Split Measures

Calculates the number of four-taxon subtrees consistent with a pair of cladograms, calculating the symmetric quartet distance of Bandelt & Dress (1986), Reconstructing the shape of a tree from observed dissimilarity data, Advances in Applied Mathematics, 7, 309-343 , and using the tqDist algorithm of Sand et al. (2014), tqDist: a library for computing the quartet and triplet distances between binary or general trees, Bioinformatics, 30, 2079–2080 for pairs of binary trees.

'Rcpp' Integration for the 'Armadillo' Templated Linear Algebra Library

'Armadillo' is a templated C++ linear algebra library aiming towards a good balance between speed and ease of use. It provides high-level syntax and functionality deliberately similar to Matlab. It is useful for algorithm development directly in C++, or quick conversion of research code into production environments. It provides efficient classes for vectors, matrices and cubes where dense and sparse matrices are supported. Integer, floating point and complex numbers are supported. A sophisticated expression evaluator (based on template meta-programming) automatically combines several operations to increase speed and efficiency. Dynamic evaluation automatically chooses optimal code paths based on detected matrix structures. Matrix decompositions are provided through integration with LAPACK, or one of its high performance drop-in replacements (such as 'MKL' or 'OpenBLAS'). It can automatically use 'OpenMP' multi-threading (parallelisation) to speed up computationally expensive operations. The 'RcppArmadillo' package includes the header files from the 'Armadillo' library; users do not need to install 'Armadillo' itself in order to use 'RcppArmadillo'. Starting from release 15.0.0, the minimum compilation standard is C++14 so 'Armadillo' version 14.6.3 is included as a fallback when an R package forces the C++11 standard. Package authors should set a '#define' to select the 'current' version, or select the 'legacy' version (also chosen as default) if they must. See 'GitHub issue #475' for details. Since release 7.800.0, 'Armadillo' is licensed under Apache License 2; previous releases were under licensed as MPL 2.0 from version 3.800.0 onwards and LGPL-3 prior to that; 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'.

Solving and Optimizing Large-Scale Nonlinear Systems

Barzilai-Borwein spectral methods for solving nonlinear system of equations, and for optimizing nonlinear objective functions subject to simple constraints. A tutorial style introduction to this package is available in a vignette on the CRAN download page or, when the package is loaded in an R session, with vignette("BB").

Disciplined Convex Optimization

An object-oriented modeling language for disciplined convex programming (DCP) as described in Fu, Narasimhan, and Boyd (2020, ). It allows the user to formulate convex optimization problems in a natural way following mathematical convention and DCP rules. The system analyzes the problem, verifies its convexity, converts it into a canonical form, and hands it off to an appropriate solver to obtain the solution. Interfaces to solvers on CRAN and elsewhere are provided, both commercial and open source.

Trust Region Optimization

Does local optimization using two derivatives and trust regions. Guaranteed to converge to local minimum of objective function.

Particle Swarm Optimization

Provides an implementation of particle swarm optimisation consistent with the standard PSO 2007/2011 by Maurice Clerc. Additionally a number of ancillary routines are provided for easy testing and graphics.

Parallel Version of the L-BFGS-B Optimization Method

Provides a parallel version of the L-BFGS-B method of optim(). The main function of the package is optimParallel(), which has the same usage and output as optim(). Using optimParallel() can significantly reduce the optimization time.

General-Purpose Unconstrained Non-Linear Optimization

An algorithm for general-purpose unconstrained non-linear optimization. The algorithm is of quasi-Newton type with BFGS updating of the inverse Hessian and soft line search with a trust region type monitoring of the input to the line search algorithm. The interface of 'ucminf' is designed for easy interchange with 'optim'.

Hyperparameter Optimization for 'mlr3'

Hyperparameter optimization package of the 'mlr3' ecosystem. It features highly configurable search spaces via the 'paradox' package and finds optimal hyperparameter configurations for any 'mlr3' learner. 'mlr3tuning' works with several optimization algorithms e.g. Random Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo') and Hyperband (in 'mlr3hyperband'). Moreover, it can automatically optimize learners and estimate the performance of optimized models with nested resampling.