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Optimization Modelling Suite
Robust, flexible optimization solvers for accurate,
efficient results.
Performant, Intuitive Mathematical Optimization
The nAG Optimization Modelling Suite provides a comprehensive collection of highly performant, flexible, solvers for use across all industries. The intuitive interface allows the modification of existing models to vastly increase cost and time efficiency as well as accuracy.
nAG optimization solvers are highly flexible and callable from many programming languages, environments, and mathematical packages.
nAG Optimization Modelling Suite
World-Class Optimization Solvers
The nAG Optimization Modelling Suite is a comprehensive collection of robust, tested and documented optimization solvers for discrete and continuous optimization available within the nAG Library.
The solvers are accessed via an intuitive interface designed for ease of use. Key areas covered include:
- Convex Optimization
- Non-Linear Programming
- Black-box & Derivative-free Optimization
- Nonlinear Least Squares & Calibration
- Global Optimization
- Mixed Integer Programming
- Faster Data Fitting
- Solving QCQP Problems
- Second-order Cone Programming
nAG Optimization Modelling Suite
Case Studies
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Why Choose the Optimization Modelling Suite?
Flexible
Designed to allow users to add, modify, and remove model components such as variables, linear constraints, least squares residuals, and more without the need to rebuild. nAG Optimization Modelling Suite is flexible like no other.
Supported
Our expert consultant developers work with the technology on a daily basis. The support nAG offer allows users to troubleshoot at speed with a simple phone call, video call, or email ensuring project operations run smoothly.
Robust
Tried and tested vigorously, nAG Optimization Modelling Suite offers unrivalled robustness and reliability that ensures user confidence.
Comprehensive
nAG constantly innovates in all areas of numerical algorithms and we continuously add to and improve our Optimization Modelling Suite.
Mathematical Optimization Solvers Coverage
The Optimization Modelling Suite – delivered with the nAG Library – features an extensive collection of Mathematical Optimization solvers, including:
Linear Programming (LP) – dense and sparse, based on simplex method and interior point method;
Quadratic Programming (QP) – convex and nonconvex, dense and sparse;
Second-order Cone Programming (SOCP) – covering many convex optimization problems, such as Quadratically Constrained Quadratic Programming (QCQP);
Nonlinear Programming (NLP) – dense and sparse, based on active-set SQP methods and interior point method (IPM);
Global Nonlinear Programming – algorithms based on multistart, branching, and metaheuristics;
Mixed Integer Linear Programming (MILP) – for large-scale problems, based on a modern branch-and-bound approach;
Mixed Integer Nonlinear Programming (MINLP) – for dense (possibly nonconvex) problems;
Semidefinite Programming (SDP) – both linear matrix inequalities (LMI) and bilinear matrix inequalities (BMI);
Derivative-free Optimization (DFO) – solvers for problems where derivatives are unavailable and approximations are inaccurate;
Least Squares (LSQ), data fitting, calibration, regression – linear and nonlinear, constrained and unconstrained.
Learn More About the Optimization Modelling Suite Capabilities
DocumentationCase Studies
Take a look at our collection of Case Studies to learn more about what the nAG Library and the Optimization Modelling Suite can do.
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