This CRAN task view contains a list of packages which offer facilities for solving optimization problems. Although every regression model in statistics solves an optimization problem they are not part of this view. If you are looking for regression methods, the following views will contain useful starting points: Multivariate, SocialSciences, Robust among others. The focus of this task view is on Optimization Infrastructure Packages , General Purpose Continuous Solvers , Mathematical Programming Solvers and Specific Applications in Optimization . Packages are categorized in these three sections.

Many packages provide functionality for more than one of the subjects listed at the end of this task view. E.g., mixed integer linear programming solvers typically offer standard linear programming routines like the simplex algorithm. Therefore following each package description a list of abbreviations describes the typical features of the optimizer (i.e., the problems which can be solved). The full names of the abbreviations given in square brackets can be found at the end of this task view under Classification According to Subject .

If you think that some package is missing from the list, please let me know.

Optimization Infrastructure Packages

• Trying to unify optimization algorithms via a single wrapper function, optimx helps to proper specify the (nonlinear) optimization problem including objective function, gradient function, and scaling. This package supports the (local) optimization of smooth, nonlinear functions with at most box constraints (bounds). optimx depends not only on packages and/or functions mentioned in this section of this task view but also on two packages implemented by the author(s), namely Rcgmin and Rvmmin. Both are "pure R" implementations of conjugate gradient minimization and variable metric nonlinear function minimization algorithms, respectively.
• The R Optimization Infrastructure (ROI) package provides a framework for handling optimization problems in R. It uses an object oriented approach to define and solve various optimization tasks in R which can be from different problem classes (e.g., linear, quadratic, non-linear programming problems). This makes optimization transparent for the R user as the corresponding workflow is completely abstracted from the underlying solver. Furthermore, this approach allows for easy switching between solvers, given that corresponding solver plugins are available, and thus enhances comparability.

General Purpose Continuous Solvers

• Package stats offers several general purpose optimization routines. First, function optim() provides an implementation of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method, bounded BFGS, conjugate gradient, Nelder-Mead, and simulated annealing (SANN) optimization methods. It utilizes gradients, if provided, for faster convergence. Typically it is used for unconstrained optimization but includes an option for box-constrained optimization. Additionally, for minimizing a function subject to linear inequality constraints stats contains the routine constrOptim(). For one-dimensional unconstrained function optimization there is optimize() which searches an interval for a minimum or maximum. Then there is nlm which is used for solving nonlinear unconstrained minimization problems. Eventually, nlminb() offers unconstrained and constrained optimization using PORT routines. [RGA, QN]
• Implementations of the augmented lagrange multiplier method for general nonlinear optimization can be found in packages alabama and Rsolnp.
• clue contains the function sumt() for solving constrained optimization problems via the sequential unconstrained minimization technique (SUMT).
• Package cmaes implements a global optimization procedure using a covariance matrix adapting evolutionary strategy (CMA-ES).
• Package dfoptim, derivative-free optimization procedures, contains quite efficient R implementations of the Nelder-Mead and Hooke-Jeeves algorithms (unconstrained and bounds-constrained). [DF]
• GA provides functions for optimization using genetic algorithms in both, the continuous and discrete case, whether constrained or not. This package allows to run corresponding optimization tasks in parallel.
• GenSA is a package providing a function for generalized simulated annealing which can be used to search for a global minimum of a very complex non-linear objective function with a very large number of optima.
• GrassmannOptim is a package for Grassmann manifold optimization. The implementation uses gradient-based algorithms and embeds a stochastic gradient method for global search.
• Package gsl provides BFGS, conjugate gradient, steepest descent, and Nelder-Mead algorithms. It uses a "line search" approach via the function multimin(). It is based on the GNU Scientific Library (GSL). [RGA, QN]
• Package maxLik provides a general purpose Newton-Raphson optimizer maxNR() as well as wrapper to methods, implemented in optim(). It supports fitting a submodel by specifying constant parameters.
• An R port of the Scilab neldermead module is packaged in neldermead offering several direct search optimization algorithms based on the simplex method.
• Package nloptr provides access to NLopt, an LGPL licensed library of various nonlinear optimization algorithms. It especially supports global optimization with routines such as DIRECT, StoGo, and others. It includes local derivative-free (COBYLA, Nelder-Mead, Subplex) and gradient-based (e.g., BFGS) methods, and also the augmented Lagrangian approach. [DF, GO, QN]
• Several derivative-free optimization algorithms are provided with package minqa. E.g., the functions bobyqa(), newuoa(), and uobyqa() allow to minimize a function of many variables by a trust region method that forms quadratic models by interpolation. bobyqa() additionally permits box constraints (bounds) on the parameters. [DF]
• The NMOF package provides implementations of differential evolution, particle swarm optimization, local search and threshold accepting (a variant of simulated annealing). The latter two methods also work for discrete optimization models, as does the implementation of a genetic algorithm that is included in the package.
• Package powell optimizes functions using Powell's UObyQA algorithm (Unconstrained Optimization by Quadratic Approximation).
• A particle swarm optimizer (PSO) is implemented in package pso. The algorithm follows the standard PSO 2007 by Maurice Clerc et al. Additionally, a number of ancillary routines are provided for easy testing and graphics. Another (parallelized) implementation of the PSO algorithm can be found in package ppso available from rforge.net .
• Package hydroPSO is a model-independent global optimization tool for real-world models, implementing the latest Standard Particle Swarm Optimization algorithm (SPSO-2011). hydroPSO is parallel-capable, and includes several fine-tuning options and post-processing functions.
• subplex provides unconstrained function optimization based on a subspace searching simplex method.
• Package ucminf implements an algorithm of quasi-Newtonian type for nonlinear unconstrained optimization. [QN]
• In package trust, a routine with the same name offers local optimization based on the "trust region" approach.
• trustOptim implements a "trust region" algorithm for unconstrained nonlinear optimization. The algorithm is optimized for objective functions with sparse Hessians. This makes the algorithm highly scalable and efficient, in terms of both time and memory footprint.
• Package nleqslv provides a function nleqslv() implementing Newton and Broyden methods with line search and trust region global strategies for solving medium sized system of nonlinear equations.
• Package genalg contains rbga(), an implementation of a genetic algorithm for multi-dimensional function optimization.
• Machine coded genetic algorithm (MCGA) provided by package mcga is a tool which solves optimization problems based on byte representation of variables.
• Package rgenoud offers genoud(), a routine which is capable of solving complex function minimization/maximization problems by combining evolutionary algorithms with a derivative-based (quasi-Newtonian) approach.
• An R implementation of the Self-Organising Migrating Algorithm (SOMA) is available in package soma. The approach of this stochastic optimization algorithm is similar to that of genetic algorithms and can be applied to any cost-minimization problem with a bounded parameter space, and is robust to local minima.
• Package DEoptim provides a global optimizer based on the differential evolution algorithm. RcppDE provides a C++ implementation of the DEoptim() function.
• Package DEoptimR provides an implementation of the jDE variant of the differential evolution stochastic algorithm for global optimization of nonlinear programming problems. It allows to handle constraints in a flexible manner.
• Package Rmalschains implements an algorithm family for continuous optimization called memetic algorithms with local search chains (MA-LS-Chains).

Mathematical Programming Solvers

This section provides an overview of open source as well as commercial optimizers. Which type of mathematical programming problem can be solved by a certain package or function can be seen from the abbreviations in square brackets. For a classification by subject see the list at the end of this task view.

• Package linprog solves linear programming problems using the function solveLP() (the solver is based on lpSolve) and can read model files in MPS format. [LP]
• In package quadprog solve.QP() solves quadratic programming problems with linear equality and inequality constraints. [QP]
• BB contains the function spg() providing a spectral projected gradient method for large scale optimization with simple constraints. It takes a nonlinear objective function as an argument as well as basic constraints. Furthermore, BB contains two functions ( dfsane() and sane()) for using the spectral gradient method for solving a nonlinear system of equations.
• In the boot package there is a routine called simplex() which realizes the two-phase tableau simplex method for (relatively small) linear programming problems. [LP]
• The CLSOCP package provides an implementation of a one step smoothing Newton method for the solution of second order cone programming (SOCP) problems.
• kernlab contains the function ipop for solving quadratic programming problems using interior point methods. [IPM, QP]
• limSolve offers to solve linear or quadratic optimization functions. [LP, QP]
• LowRankQP primal/dual interior point method solving quadratic programming problems [IPM, QP]
• Package rcdd offers the function lpcdd() for solving linear programs with exact arithmetic using the GNU Multiple Precision (GMP) library. [LP]
• In package Rdonlp2 (see the rmetrics project) function donlp2(), a wrapper for the DONLP2 solver, offers the minimization of smooth nonlinear functions and constraints. DONLP2 can be used freely for any kind of research purposes, otherwise it requires licensing. [GO, NLP]
• The NEOS Server for Optimization provides online access to state-of-the-art optimization problem solvers. Package rneos enables the user to pass optimization problems to NEOS and retrieve results within R.
• An interface to Ipopt from the COIN-OR initiative called ipoptr is available from R-Forge . Ipopt is an open source solver for large-scale nonlinear continuous optimization. [NLP]

Interfaces to Open Source Optimizers

• Package clpAPI provides high level access from R to low-level API routines of the COIN OR Clp solver library. [LP]
• Package lpSolve contains the routine lp() to solve LPs and MILPs by calling the freely available solver lp_solve . This solver is based on the revised simplex method and a branch-and-bound (B&B) approach. It supports semi-continuous variables and Special Ordered Sets (SOS). Furthermore lp.assign() and lp.transport() are aimed at solving assignment problems and transportation problems, respectively. Additionally, there is the package lpSolveAPI which provides an R interface to the low level API routines of lp_solve (see also project lpsolve on R-Forge ). lpSolveAPI supports reading linear programs from files in lp and MPS format. [BP, IP, LP, MILP, SPLP]
• Packages glpkAPI as well as package Rglpk provide an interface to the GNU Linear Programming Kit (GLPK). Whereas the former provides high level access to low level routines the latter offers a high level routine Rglpk_solve_LP() to solve MILPs using GLPK. Both packages offer the possibility to use models formulated in the MPS format. [BP, IP, IPM, LP, MILP]
• Rsymphony provides the routine Rsymphony_solve_LP() which interfaces the SYMPHONY solver for mixed integer linear programs. SYMPHONY applies LP relaxation with a branch-and-cut approach. It is part of the Computational Infrastructure for Operations Research (COIN-OR) project which is an initiative to spur the development of open source software for the operations research community. [LP, IP, MILP]
• The NOMAD solver is implemented in the crs package for solving mixed integer programming problems. This algorithm is accessible via the snomadr() function and is primarily designed for constrained optimization of blackbox functions.
• The CSDP semidefinite programming library is interfaced by package Rcsdp. [SDP]

Interfaces to Commercial Optimizers

This section surveys interfaces to commercial solvers. Typically, the corresponding libraries have to be installed separately.

• Packages cplexAPI and Rcplex provide an interface to the CPLEX solver package from IBM . CPLEX provides dual/primal simplex optimizers as well as a barrier optimizer for solving large scale linear and quadratic programs. Furthermore, it offers a mixed integer optimizer to solve difficult mixed integer programs. Note that CPLEX is not free and you have to get a license from IBM . Academics will receive a free licence upon request. [LP, IP, BP, QP, MILP, MIQP, IPM]
• Package Rmosek offers an interface to the commercial optimizer from MOSEK . It provides dual/primal simplex optimizers as well as a barrier optimizer. In addition to solving LP and QP problems this solver can handle SOCP and quadratically constrained programming (QPQC) tasks. Furthermore, it offers a mixed integer optimizer to solve difficult mixed integer programs (MILP, MISOCP, etc.). Note that you have to get a license from MOSEK . Academic licenses are free of charge. [LP, IP, BP, QP, MILP, MIQP, IPM]
• Gurobi Optimization ships an R binding with their 5.0 release that allows to solve LP, MIP, QP, MIQP, SOCP, and MISOCP models from within R. See the R reference manual from the Gurobi documentation for details.

Specific Applications in Optimization

• Package adagio provides functions for single and multiple knapsack problems and solves subset sum and assignment tasks.
• In package clue solve_LSAP() enables the user to solve the linear sum assignment problem (LSAP) using an efficient C implementation of the Hungarian algorithm. [SPLP]
• The data cloning algorithm is a global optimization approach and a variant of simulated annealing which has been implemented in package dclone. The package provides low level functions for implementing maximum likelihood estimating procedures for complex models using data cloning and Bayesian Markov chain Monte Carlo methods.
• Package goalprog provides some functions for lexicographic linear goal programming and optimization. Goal programming is a branch of multi-objective, multi-criteria decision analysis. [MOP]
• igraph, a package for graph and network analysis, uses the very fast igraph C library. It can be used to calculate shortest paths, maximal network flows, minimum spanning trees, etc. [GRAPH]
• Package minpack.lm provides an interface to the Levenberg-Marquardt nonlinear least-squares algorithm found in MINPACK.
• maxLik adds a likelihood-specific layer on top of a number of maximization routines like Brendt-Hall-Hall-Hausman (BHHH) and Newton-Raphson among others. It includes summary and print methods which extract the standard errors based on the Hessian matrix and allows easy swapping of maximization algorithms. It also provides a function to check whether an analytic derivative is computed directly.
• Multi-criteria optimization problems can be solved using package mco which implements genetic algorithms. [MOP]
• Package quantreg contains variations of simplex and of interior point routines ( nlrq(), crq()). It provides an interface to L1 regression in the R code of function rq(). [SPLP, LP, IPM]
• Package sna contains the function lab.optim() which is the front-end to a series of heuristic routines for optimizing some bivariate graph statistic. [GRAPH]
• Package TSP provides basic infrastructure for handling and solving the traveling salesperson problem (TSP). The main routine solve_TSP() solves the TSP through several heuristics. In addition, it provides an interface to the Concorde TSP Solver , which has to be downloaded separately. [SPLP]

Classification According to Subject

What follows is an attempt to provide a by-subject overview of packages. The full name of the subject as well as the corresponding MSC 2010 code (if available) are given in brackets.

• LP (Linear programming, 90C05): boot, clpAPI, cplexAPI, glpkAPI, limSolve, linprog, lpSolve, lpSolveAPI, quantreg, rcdd, Rcplex, Rglpk, Rmosek, Rsymphony
• GO (Global Optimization): nloptr
• SPLP (Special problems of linear programming like transportation, multi-index, etc., 90C08): clue, lpSolve, lpSolveAPI, quantreg, TSP
• BP (Boolean programming, 90C09): cplexAPI, glpkAPI, lpSolve, lpSolveAPI, Rcplex, Rglpk
• IP (Integer programming, 90C10): cplexAPI, glpkAPI lpSolve, lpSolveAPI, Rcplex, Rglpk, Rmosek, Rsymphony
• MIP (Mixed integer programming and its variants MILP for LP and MIQP for QP, 90C11): cplexAPI, glpkAPI, lpSolve, lpSolveAPI, Rcplex, Rglpk, Rmosek, Rsymphony
• QP (Quadratic programming, 90C20): cplexAPI, kernlab, limSolve, LowRankQP, quadprog, Rcplex, Rmosek
• SDP (Semidefinite programming, 90C22): Rcsdp
• MOP (Multi-objective and goal programming, 90C29): goalprog, mco
• NLP (Nonlinear programming, 90C30): Rdonlp2, Rsolnp
• GRAPH (Programming involving graphs or networks, 90C35): igraph, sna
• IPM (Interior-point methods, 90C51): cplexAPI, kernlab, glpkAPI, LowRankQP, quantreg, Rcplex
• RGA (Methods of reduced gradient type, 90C52): stats ( optim()), gsl
• QN (Methods of quasi-Newton type, 90C53): stats ( optim()), gsl, nloptr, ucminf
• DF (Derivative-free methods, 90C56): dfoptim, minqa, nloptr