Search Results for "optimization" - Page 2
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Showing 57 open source projects for "optimization"
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Opt4J
Modular Java framework for meta-heuristic optimization
Opt4J is an open source Java-based framework for evolutionary computation. It contains a set of (multi-objective) optimization algorithms such as evolutionary algorithms (including SPEA2 and NSGA2), differential evolution, particle swarm optimization, and simulated annealing. The benchmarks that are included comprise ZDT, DTLZ, WFG, and the knapsack problem. The goal of Opt4J is to simplify the evolutionary optimization of user-defined problems as well as the implementation of arbitrary meta-heuristic optimization algorithms. ... -
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UNLocBoX
UNLocBox (Matlab convex optimization toolbox)
The UNLocboX is a matlab convex optimization toolbox part of the UnlocX project. It is composed of the most used algorithms such as forward backward, Douglas-Rachford, admm or ppxa. Moreover a collection of proximal operators is available in order to implement problems very efficiently. -
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NEW OPTIMIZATION TECHNOLOGY & PLANNING EXPERIMENT. Technology is designed for multidimensional optimization practical problems with continuous object functions. Technology higher efficiency than traditional optimization methods.
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ViennaCL
Linear algebra and solver library using CUDA, OpenCL, and OpenMP
ViennaCL provides high level C++ interfaces for linear algebra routines on CPUs and GPUs using CUDA, OpenCL, and OpenMP. The focus is on generic implementations of iterative solvers often used for large linear systems and simple integration into existing projects. -
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The Java Data Mining Package (JDMP) is a library that provides methods for analyzing data with the help of machine learning algorithms (e.g. clustering, classification, graphical models, neural networks, Bayesian networks, text processing, optimization).
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MOEA Framework
A Free and Open Source Java Framework for Multiobjective Optimization
The MOEA Framework is a free and open source Java library for developing and experimenting with multiobjective evolutionary algorithms (MOEAs) and other general-purpose multiobjective optimization algorithms. The MOEA Framework supports genetic algorithms, differential evolution, particle swarm optimization, genetic programming, grammatical evolution, and more. A number of algorithms are provided out-of-the-box, including NSGA-II, NSGA-III, ε-MOEA, GDE3 and MOEA/D. In addition, the MOEA Framework provides the tools necessary to rapidly design, develop, execute and statistically test optimization algorithms. -
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This is a sophisticated & integrated simulation and analysis environment for dynamical systems models of physical systems (ODEs, DAEs, maps, and hybrid systems). It supports symbolic math, optimization, continuation, data analysis, biological apps...
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Finds communities in networks with a submodular algorithm based on the optimization of Newman's modularity. The algorithm finds a weakly optimal community partition of the network. Developed by J. R. Busch, M. G. Beiro and J. I. Alvarez-Hamelin,
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JBernstein
JBernstein: Exploration techniques for non-linear constraint checking
...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. Although JBernstein uses double, it offers automatic error-estimation capabilities (experimental) so that the generated result is correct (the solver may return unknown when the error-estimate is too large) -
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PyGAO
Genetic Algorithm Optimization for Python
A simple interface for performing genetic algorithm optimization for numerical problems. I am starting with a stripped-down version, where a solution can be described using a single vector of float numbers. Eventually, I will expand to more generic data structures and add multiple-species search options. For the time being, I have no plans of developing a GUI. For now, this is strictly a computational module. -
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Math tools in Python to tackle down problems in Operational Research fields. Comes with a Django based web interface to allow remote access to complex simulation means.
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Semantic query optimization (SQO) is the process of finding equivalent rewritings of an input query given constraints that hold in a database instance. We present a Chase & Backchase (C&B) algorithm strategy that generalizes and improves on well-known methods in the field. The implementation of our approach, the pegasus system, outperforms existing C&B systems an average by two orders of magnitude.
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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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f2what
Lapack/quadprog QP for VBA/.Net/Java
...It also offers already translated versions of Lapack and quadprog to Java, .Net and VBA, to give access to SVD, LU, QR, Cholesky, eigenvalue decomposition, linear system solving and QP optimization in those three languages. -
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Tcl binding to levmar library implementing Levenberg-Marquardt optimization algorithm often used in data fitting. Levmartcl is a standard Tcl extension providing command interface to levmar functionality. This library is only a wrapper of <levmar> which actually performs the work. See the <levmar> home site for details: http://www.ics.forth.gr/~lourakis/levmar/
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...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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Substitution cipher cracker based on ant colony optimization metaheuristic. Includes GUI. Everything is in czech because this is a school project :(
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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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Tidal Analysis Program in PYthon (TAPPY) uses the least squares optimization function from scipy to perform a harmonic analysis (calculate amplitude and phases of a set of sine waves) of a hourly time series of water level values.
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Experimental C++ library for performing optimized fixed-point operations using types of differing sizes and precisions. Requires C++0x extensions.
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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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LSGTL means LLX’s Static Graph Template Library which is a light-weighted header-only template library developed mainly for static graph analysis. LSGTL is expected to be used in laboratories for research purposes mostly.
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This program generates customizable hyper-surfaces (multi-dimensional input and output) and samples data from them to be used further as benchmark for response surface modeling tasks or optimization algorithms.
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The Open Optimization for Java provides a framework and the implementation of commonly-used algorithms found in Graph Theory and Network Optimization, e.g. shortest path and postman problem.
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Combean is a Java framework for mathematical structures and optimization algorithms. Through a set of Java interfaces and JavaBeans-based config, Combean glues optimization codes together - leading to more interoperable, adaptable and flexible solutions.
