
WebCab Optimization for .NET
Refined procedures for solving and performing sensitivity analysis on uni and multi dimensional, local or global
optimization problems which may or may not have linear constraints. Specialized Linear programming algorithms based on the
Simplex Algorithm and duality are included along with a framework for sensitivity analysis w.r.t. boundaries (duality, or
direct approach), or object function coefficients.
Software Information 
System Requirements 
Version: 
v2.60 
 Windows 9x/NT/2000/XP/2003
 Pentium II® 500Mhz
 .NET Framework v1.x

File Size: 
Full version: 4.19 MB 
License:  Free to try, $ 179 to buy 
Rating :  
This suite includes the following features:
 Local unidimensional optimization  finds
global minima / maxima for continuous functions in one dimension
 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 Ndimensional space
 FletcherReeves  an implementation of the conjugate gradient method
 PolakRiviere  an implementation of the conjugate gradient method
 Variable metric algorithms/QuasiNewton 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.
 FletcherPowell  an implementation of the variable metric algorithm
 BroydenFletcherGoldfarbShanno  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 KuhnTucker 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.
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