Presents original and review papers on all aspects of numerical algorithms
Coverage includes new algorithms, theoretical results, implementation, numerical stability, complexity, parallel computing, subroutines and applications
Also provides book reviews and announcements of scientific meetings
The journal Numerical Algorithms presents original and review papers on all aspects of numerical algorithms: new algorithms, theoretical results, implementation, numerical stability, complexity, parallel computing, subroutines and applications. Papers on computer algebra related to obtaining numerical results also included. The journal offers high quality papers containing material not published elsewhere. The journal also provides book reviews and announcements of scientific meetings.
Fræser is a framework for estimating the parameters of static and dynamic errors-in-variables systems with the opportunity to compare various errors-in-variables parameter estimation algorithms via simulations. It features a graphical user interface and several examples for simultaneously estimating model and noise parameters.
The framework incorporates the following linear and nonlinear estimation methods for static and dynamic systems:
* model parameter estimation for static systems
o Koopmans method
* linear model and noise parameter estimation for dynamic systems
o (extended) instrumental variables method (XIV)
o bias-compensating least-squares method (BCLS)
o Frisch scheme (FS)
o generalized Koopmans-Levin method (GKL)
* nonlinear model parameter estimation for static systems
o nonlinear Koopmans method (NK)
o approximated maximum likelihood method (AML)
* nonlinear model and noise parameter estimation for dynamic systems
o bias-compensated least squares method (BCLS)
o nonlinear Koopmans-Levin method (NKL)
o nonlinear extennonlinear extension to generalized Koopmans-Levin method (NGKL)
Evocosm is a set of classes that abstract the fundamental components of an evolutionary algorithm. I'll list the components here with a bit of introduction; you can review the details of the classes by downloading the code archives or by reviewing the online documentation (see the menu at the article's beginning for code and documentation links.) All class documentation was generated from source code comments using doxygen. These docs have not been thoroughly proofread, so they may contain a few typos and minor errors. Self-publishing has taught me the value of a good proofreader... ;} Evolutionary algorithms come in a variety of shapes and flavors, but at their core, they all share certain characteristics: populations that reproduce and mutate through a series of generations, producing future generations based on some measure of fitness. An amazing variety of algorithms can be built on that general framework, which leads me to construct a set of core classes as the basis for future applications.
EvA2 (an Evolutionary Algorithms framework, revised version 2) is a comprehensive heuristic optimization framework with emphasis on Evolutionary Algorithms implemented in Java. It is a revised version of the JavaEvA optimization toolbox, which has been developed as a resumption of the former EvA software package. EvA2 integrates several derivation free optimization methods, preferably population based, such as Evolution Strategies (ES), Genetic Algorithms (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), as well as classical techniques such as multi-start Hill Climbing or Simulated Annealing. Besides typical single-objective problems, multi-modal and multi-objective problem are handled directly by the EvA2 framework. Via the Java mechanism of Remote Method Invocation (RMI), the algorithms of EvA2 can be distributed over network nodes based on a client-server architecture. EvA2 aims at two groups of users. Firstly, the end user who does not know much about the theory of Evolutionary Algorithms, but wants to use Evolutionary Algorithms to solve an application problem. Secondly, the scientific user who wants to investigate the performance of different optimization algorithms or wants to compare the effect of alternative or specialized evolutionary or heuristic operators. The latter usually knows more about evolutionary algorithms or heuristic optimization and is able to extend EvA2 by adding specific optimization strategies or solution representations. EvA2 is being used as teaching aid in lecture tutorials, as a developing platform in student research projects and applied to numerous optimisation problems within active research and ongoing industrial cooperations.
ClusterViz is a software to visualize the clustering process using the family of k-means algorithms. The program is free software under the GNU General Public License (GPL). ClusterViz allows to cluster data while visualizing an up to three dimensional projection. The clustering process is visualized using OpenGL. As clustering algorithms the family of k-means algorithms is implemented, including mixture models.
Fast Artificial Neural Network Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution
JOpt.SDK is a vehicle routing software library for Java that uses specialized genetic algorithms to calculate an optimized allocation of orders and stops to mobile resources. The algorithm not only provides tours at minimum costs but also considers an arbitrary set of constraints for each tour. You may define your own constraints and optimization goals in order to customize JOpt.SDK to your specific planning needs or you decide to use one of our best practices addons in order to achieve a fast application of our optimization algorithms to selected industries.
JOpt.SDK can solve nearly any problem that can be classified by one of the following types:
* TSP - Traveling Salesman Problem. JOpt.SDK finds the shortest or fastest path for your mobile resources
* VRPTW - Vehicle routing problem with time windows - like TSP but for a set of vehicles. JOpt.SDK finds an optimal allocation of orders and stops within a vehicle fleet. It may also consider different constraints for vehicles, drivers and stops.
JOpt.SDK functionality can be accessed via Java API and thus fits seamlessly into any JAVA application. Software developers may integrate the JOpt.SDK component into their application in order to offer their customers a consistent solution including optimization of mobile workforce schedules. A seamless integration into your software allows the look and feel of one piece of software for your customer.
The following is a list of the algorithms described in Wikipedia. See also the list of data structures, list of algorithm general topics and list of terms relating to algorithms and data structures.