Search Results for "nonlinear optimization"
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Showing 16 open source projects for "nonlinear optimization"
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JuMP
Modeling language for Mathematical Optimization
JuMP is a modeling language and collection of supporting packages for mathematical optimization in Julia. JuMP makes it easy to formulate and solve a range of problem classes, including linear programs, integer programs, conic programs, semidefinite programs, and constrained nonlinear programs. JuMP is used to solve large-scale inventory routing problems at Renault, schedule trains at Thales Inc., plan power grid expansion at PSR, and route school buses. -
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Adaptive Simulated Annealing (ASA)
simulated annealing optimization and importance-sampling
Adaptive Simulated Annealing (ASA) is a C-language code that finds the best global fit of a nonlinear cost-function over a D-dimensional space. ASA has over 100 OPTIONS to provide robust tuning over many classes of nonlinear stochastic systems. -
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LMath library
Math library for Free Pascal and Lazarus.
...It is entirely written in Pascal and does not depend on external libraries. LMath provides routines and demo programs for numerical analysis, including mathematical functions, probabilities, matrices, optimization, linear and nonlinear equations, integration, Fast Fourier Transform, random numbers, curve fitting, statistics and graphics. It is organized s set of lazarus packages which makes it easily extensible and helps to include only really needed features in your project. -
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GRAMPC
A gradient-based augmented Lagrangian framework for embedded NMPC
GRAMPC is a nonlinear MPC framework that is suitable for dynamical systems with sampling times in the (sub)millisecond range and that allows for an efficient implementation on embedded hardware. The algorithm is based on an augmented Lagrangian formulation with a tailored gradient method for the inner minimization problem. GRAMPC is implemented in plain C with an additional interface to C++ and MATLAB/Simulink. -
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Algorithms Math Models
MATLAB implementations of algorithms
Algorithms_MathModels is a large MATLAB collection of algorithms and solved examples targeted at students and teams preparing for mathematical modeling competitions (national and international contests like MCM/ICM). The repository gathers implementations and case studies across many topics commonly used in contest solutions: optimization (linear, integer, goal and nonlinear programming), heuristic and metaheuristic methods (simulated annealing, genetic algorithms, immune algorithms), neural networks and time-series methods, interpolation and regression, graph theory, cellular automata, grey systems, fuzzy models, partial/ordinary differential equations, and multivariate analysis, among others. ... -
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NOMAD is a C++ code that implements the MADS algorithm (Mesh Adaptive Direct Search) for difficult blackbox optimization problems. Such problems occur when the functions to optimize are costly computer simulations with no derivatives.
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GADfit
Global nonlinear optimization with automatic differentiation
NOTE: Development continued at GitHub! Current version is higher than 1.2.0. GADfit is a Fortran implementation of global nonlinear curve fitting, based on a modified Levenberg-Marquardt algorithm. Global fitting refers to fitting many datasets simultaneously with some parameters shared among the datasets. The fitting procedure is very fast and accurate thanks to the use of automatic differentiation. The model curves (fitting functions) can be of essentially arbitrary complexity. This... -
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OptimC
OptimC - Optimization / Unconstrained Minimization Library in ANSI C
OptimC is a C software package to minimize any unconstrained multivariable function. The algorithms implemented are Nelder-Mead,Newton Methods (Line Search and Trust Region methods), Conjugate Gradient and BFGS (regular and Limited Memory). Brent method is also available for single variable functions if the bounds are known. Update 06/09/2014 - Nonlinear Squares Implementation [Levenberg-Marquardt Method] Added. Documentation - http://code.google.com/p/optimc/ -
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JBernstein
JBernstein: Exploration techniques for non-linear constraint checking
JBernstein is a tool implemented in Java that enables to check whether a polynomial constraint holds for all variables within their domain. It is mainly used in the verification or synthesis of cyber-physical systems where the system model is nonlinear. The tool also supports specifications in assume-guarantee style. The underlying algorithm is based on a translation to Bernstein polynomials, and several optimization techniques are implemented to ensure efficiency. Concerning performance, the tool enables to check all examples in the PVS Bernstein testsuite within a second, which can be an order of magnitude faster than some other tools such as QEPCAD or REDLOG. ... -
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Spreadsheet Template for Structural Engineering (FEA Analysis for beams, trusses, 2D-frames). It makes possible to create your own parametric models.Templates for engineering calculation (fatigue, concrete plates, etc)
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Large Scale Optimization Templates
C++ templates with generic nonlinear optimization algorithms
Highly tunable, simple to use collection of the templates, containing a set of classes for solving unconstrained large scale nonlinear optimization problems. Currently it contains: -- Limited Memory Quasi Newton (L-BFSG) -- BFSG -- Conjugate Gradient -- Gradient Descent -- Wolf condition Line Search -- Backtracking Line Search -- Exact Golden Search -- Golden Search with Wolf condition We also distribute a set of tests with the library. -
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ACADO Toolkit
Toolkit for Automatic Control and Dynamic Optimization
ACADO Toolkit is a software environment and algorithm collection for automatic control and dynamic optimization. It provides a general framework for using a great variety of algorithms for direct optimal control, including model predictive control, state and parameter estimation and robust optimization. ACADO Toolkit is implemented as self-contained C++ code and comes along with user-friendly MATLAB interface. The object-oriented design allows for convenient coupling of existing optimization... -
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...MTL was used to build a 3d Scanner. MTL consists of pars B - Basic Functions, Matrices, Images, Hypermodels (3d Models and up) N - Numeric Functions ranging from linear regression over nonlinear optimization to singular-value computation I - Image filters and Image enhancement H - Hardware related (optional part), does require additional libraries and is only useful on certain hosts. G - Hyper-Model functions such as ray-plane intersections etc.
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OptLib
C nonlinear optimization library
[PROJECT MIGRATED TO GIT-HUB] OptLib is a library of nonlinear optimization routines focused on the use of stochastic methods, including Simulated Annealing, Genetic Algorithms, and Monte Carlo. Routines are parallelized using MPI. -
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A Python environment for large and sparse linear and nonlinear optimization using intuitive interfaces to linear algebra tools and subproblem solvers written in low-level languages. It provides building blocks to experiment with new ideas and algorithms.
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H2O is a collection of C routines and applications designed for the optimization of multi-reservoir and multi-period hydrothermal power production scheduling. H2O is built upon nonlinear network algorithms and heuristic techniques for higher efficiency.
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