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The R Optimization Infrastructure (ROI) package provides a sophisticated framework for handling optimization problems in R.


An interface to the MOSEK optimization library designed to solve large-scale mathematical optimization problems. Supports linear, quadratic and second order cone optimization with/without integer variables, in addition to the more general separable convex problems. Trial and free academic licenses available at


Within this package the XML-RPC API to NEOS is implemented. This enables the user to pass optimization problems to NEOS and retrieve results within R.


This package provides an R implementation of the Self-Organising Migrating Algorithm, a general-purpose, stochastic optimisation algorithm. The approach is similar to that of genetic algorithms, although it is based on the idea of a series of ``migrations'' by a fixed set of individuals, rather than the development of successive generations. It can be applied to any cost-minimisation problem with a bounded parameter space, and is robust to local minima.


This package provides an efficient C++ based implementation of the DEoptim function which performs global optimization by differential evolution. Its creation was motivated by trying to see if the old approximation "easier, shorter, faster: pick any two" could in fact be extended to achieving all three goals while moving the code from plain old C to modern C++. The initial version did in fact do so, but a good part of the gain was due to an implicit code review which eliminated a few inefficiencies which have since been eliminated in DEoptim.


The package provides an implementation of PSO consistent with the standard PSO 2007/2011 by Maurice Clerc et al. Additionally a number of ancillary routines are provided for easy testing and graphics.


Provides a replacement and extension of the optim() function to unify and streamline optimization capabilities in R for smooth, possibly box constrained functions of several or many parameters. This is the CRAN version of the package.


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


Provides several direct search optimization algorithms based on the simplex method. The provided algorithms are direct search algorithms, i.e. algorithms which do not use the derivative of the cost function. They are based on the update of a simplex. The following algorithms are available: the fixed shape simplex method of Spendley, Hext and Himsworth (unconstrained optimization with a fixed shape simplex), the variable shape simplex method of Nelder and Mead (unconstrained optimization with a variable shape simplex made), and Box's complex method (constrained optimization with a variable shape simplex).


Single objective optimization using a CMA-ES.