WebCab Optimization v2.5

Solve complex one and multi dimensional optimizations problems efficiently with our powerful optimization component. This component offers advanced algorithms covering local and global unidimensional optimization, local multidimensional optimization with or without constraints. We also include a method for solving liner programming prooblems known as the simplex method.

New Self-Deploy By clicking the self-install package the EJB component will be automatically deployed onto a supported local EJB application server (see details).

Prices
WebCab Optimization v2.5
Single CPU Server License
 $499
2 CPU Server License
 $658
4 CPU Server License
 $988
Unlimited Site Wide/Company Server License
 $1,383
Demo License (limited functionality)
 $0
Prices are expressed in USD.
Product Details

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 algortihms-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 efficent to use
        • Cubic interpolation locate-very fast algorithm with reasonable accuracy; requires the derivatives to be known
        • Brent method for derivable functions-medium speed; good accuracy; requires derivatives
    • 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 (1000 to 10000 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.
      • Methods for smooth functions
      • Methods for derivable functions
  • Global unidimensional optimization-finds global minima / maxima.
    • Methods for smooth functions
    • Methods for derivable functions
  • Unconstrained local multidimensional optimization
    • Methods for general functions-these algorithms do not require continuous functions
      • The downhill simplex method of Nelder and Mead-minimizes the function over a sequence of equal volume simplexes
    • Methods for smooth functions
      • Conjugate direction algorithms
        • Powell's method
    • 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 algorthms-speed and accuracy highly dependent on the particular function, these methods can be decieved by `valleys' in the N-dimensional space
        • Fletcher-Reeves-an impementation of the conjugate gradient method
        • Polak-Riviere-an implementatin of the conjugate gradient method
      • Variable metric algorithms-algorithms/Quasi-Newton algorithms - slow speed; good results on a large class of continuous functions. The basic idea is to nd 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
    • The simplex algorithm-Kuenzi, Tszchach and Zehnder implementation of the simplex algorithm for linear programming

This package also contains the following features:

  • GUI Bundle - we bundle a suite of graphical user interface JavaBean components (with a site-wide licence) allowing the developer to plug-in a wide range of GUI functionality (including charts/graphs) into their client applications
  • UML Models - to assist software architects we include UML diagrams
  • EAR Files - we provide individual customized EAR files for the most widely used application servers including IBM WebSphere 4.0/5.0, BEA WebLogic 6.1/7.0, Oracle 9iAS, Sun ONE AppServer 7, Ironflare Orion 1.5.2/1.6.0, Borland AppServer 5.0, Sybase EAServer v3.6, JBoss 2.4.4/3.0.0
  • 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

 

Prerequisites

  • An Operating System running JavaTM
  • Pentium III® 733 Mhz
  • 256MB RAM
  • A J2EE1.3 (EJB2.0) compatible Application Server

Software requirements:
  • Java2 Enterprise Edition
  • JDK 1.3 or compatible
Compatibility
Operating system for deployment:
  • Windows XP, 2000, NT
  • Sun Solaris
  • Linux
  • IBM AIX
  • HP-UX

Built Using:

  • JavaTM 2 SDK Standard Edition 1.3.1/1.4
  • JavaTM 2 SDK Enterprise Edition 1.3
Application Servers:
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