Semidefinite Programming (SDP) – both linear matrix inequalities (LMI) and bilinear matrix inequalities (BMI).Mixed Integer Nonlinear Programming (MINLP) – for dense (possibly nonconvex) problems.Global Nonlinear Programming – algorithms based on branching, multistart and stochastic optimization.Nonlinear Programming (NLP) – dense and sparse, based on active-set SQP methods and interior point method (IPM).Second-order Cone Programming (SOCP) – covering many convex optimization problems, such as Quadratically Constrained Quadratic Programming (QCQP).Quadratic Programming (QP) – convex and nonconvex, dense and sparse.Linear Programming (LP) – dense and sparse.The main classes of optimization problems covered in the NAG Library are: They cover a wide set of problems and circumstances so users do not feel limited by their model.
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Nag definition software#
There might be more than one way to formulate an optimization problem into a mathematical model and each type of model requires a specific optimization solver - NAG offers a comprehensive collection of optimization solvers so users have all they need in one place. NAG solvers are backed by over five decades of experience in developing numerical software and are supported by collaborations with many leading academics and universities. Fortran Library for SMP & Multicore Versions.
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