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Summary

WebCab Optimization (J2SE Edition) by WebCab

URLs: webcab-optimization-j2se, webcab optimization j2se, webcaboptimizationj2se, webcab

Java API containing refined procedures for solving sensitivity analysis on uni and multi dimensional, local or global optimization problems. WebCab Optimization includes Specialized Linear programming algorithms based on the Simplex Algorithm and duality, are included.

This suite includes the following features:

Local unidimensional optimization - finds global minima / maxima for continuous functions in one dimension

Fast `low level' algorithms - use these algorithms when your primary concern is the speed and not the accuracy of the results. You will have to chose one bracketing algorithm and one locate algorithm (note, they are useful only in pairs). Also you will have to manually provide a lot of parameters (tolerance, maximum cycles etc) which can dramatically change the algorithm performance

Bracketing algorithms - these methods find an interval where at least one extrema of a continuous function exists

Acceleration bracketing - this method can be used with any continuous functions

Parabolic extrapolation bracketing - gives better results than acceleration bracketing for a large class of functions (functions that are locally parabolic about the extrema)

Acceleration bracketing for derivable functions - requires derivatives to be known; it's slower than the general acceleration algorithm but also safer

Locate algorithms - these methods converge to the extrema if the extrema is bracketed and the function under consideration is continuous

Parabolic interpolation locate - very fast algorithm but with moderate accuracy

Linear locate - slow algorithm but exhibits stable convergence

Brent locate - medium speed with good accuracy. With a good balance of speed and accuracy, this algorithm is very efficient to use

Cubic interpolation locate - very fast algorithm with reasonable accuracy; requires the derivatives to be known

Brent method for derivable functions - medium speed and good accuracy but requires derivatives to be known

Accurate `high level' algorithms - these algorithms are easy to use and offer high accuracy but are also very slow compared with the `low 'level' algorithms above (1,000 to 10,000 times slower). Use these algorithms when you need reliable results. The probability for a `high level' algorithm to make a mistake is much less than that of `low level' algorithms.

Method for continuous functions

Method for derivable functions

Global unidimensional optimization - finds global minima / maxima.

Methods for continuous functions

Methods for derivable functions

Unconstrained local multidimensional optimization

Methods for general functions - these algorithms do not require continuous functions

Downhill simplex method of Nelder and Mead - minimizes the function over a sequence of equal volume simplexes

Methods for continuous functions - these algorithms require the function to be continuous

Conjugate direction algorithms - this algorithm searches by iterating along conjugate paths

Powell's method - an implementation of the conjugate direction algorithm

Methods for derivable functions - these algorithms require the gradient of the function to be known

Steepest descent - a classical method with poor results, this method should mainly be used for testing purposes

Conjugate gradient algorithms - speed and accuracy highly dependent on the particular function, these methods can be deceived by `valleys' in the N-dimensional space

Fletcher-Reeves - an implementation of the conjugate gradient method

Polak-Riviere - an implementation of the conjugate gradient method

Variable metric algorithms/Quasi-Newton algorithms - slow speed; good results on a large class of continuous functions. The basic idea is to find the sequence of matrices which converges to the inverse Hessian of the function.

Fletcher-Powell - an implementation of the variable metric algorithm

Broyden-Fletcher-Goldfarb-Shanno - an implementation of the variable metric algorithm

Unconstrained global multidimensional optimization

Simulated annealing - a technique that has attracted significant attention as suitable for optimizing problems of large scale, especially ones where a desired global extremum is hidden among many poorer, local extrema

Constrained optimization for derivable functions with linear constraints

Rosen's gradient projection algorithm - uses the Kuhn-Tucker conditions as a termination criteria.

Linear programming - here the functions are linear and the constraints are linear

Simplex algorithm - Kuenzi, Tszchach and Zehnder implementation of the simplex algorithm for linear programming

Duality - Construct and solve the dual problem for a given primal linear programming problem.

Sensitivity Analysis - Study how the location and value of the extremum varies under perturbations of the object function and parallel shifts of the linear constraints. Sensitivity analysis of the boundaries can very efficient be carried out with the application a duality techniques.

Sensitivity Analysis - Stability of the value and location of the extremum

General Framework - Perform sensitivity analysis on any optimization problem/algorithm combination.

Flexibility - Perform sensitivity analysis on the object function, constraints and/or algorithm.

This product also contains the following features:

GUI Bundle - we bundle a suite of graphical user interface JavaBean components (with 1, 2, 4 or site-wide license) allowing the developer to plug-in a wide range of GUI functionality (including charts/graphs) into their client applications

Self-Deploy - the relevant servers EAR file will be self-deployed onto supported local application servers during the installation of the self-install package. The supported application servers include IBM WebSphere 4.0/5.0, BEA WebLogic 6.1/7.0, Oracle 9iAS, Borland AppServer 5.0, Ironflare Orion 1.5.2/1.6.0 and JBoss 2.4.4/3.0.0

UML Models - to assist system architects we provide UML diagrams of this component

Java API containing refined procedures for solving sensitivity analysis on uni and multi dimensional, local or global optimization problems.

Pricing: WebCab Optimization (J2SE Edition) V2.6 1 Developer License, WebCab Optimization (J2SE Edition) V2.6 4 Developer Team License, WebCab Optimization (J2SE Edition) V2.6 1 Site Wide License (Allows Unlimited Developers at a Single Physical Address)

Evals & Downloads: WebCab Optimization (J2SE Edition) documentation - Requires Acrobat Reader, Download the WebCab Optimization (J2SE Edition) evaluation on to your computer - Expires after 100 uses

Operating System for Deployment: Windows XP, Windows Server 2003, Windows 2000, Windows 98, Windows NT 4.0, Sun Solaris 9, Linux Kernel V2.4.x, RedHat Linux 7.x, SUSE Linux 8.x

Architecture of Product: 32Bit

Product Type: Component

Component Type: JavaBean, Java Class, WebLogic Workshop 8.1 Java Control

Compatible Containers: JBuilder X, JBuilder 9, JBuilder 7, JBuilder 6, Oracle 9i JDeveloper, NetBeans IDE 3.x, Sun ONE Studio 4 (Formerly FORTE for Java), WebLogic Workshop, IBM WebSphere Studio

Product Class: Business Components

Keywords: optimization solver linear programming quadratic

Math Stats Mathematics Mathematical Statistic Statistical

Part numbers: PC-515469-51257 515469-51257 PC-515469-51258 515469-51258 PC-515469-51259 515469-51259