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Auto2Fit 3.0

Size

 MB

License

 Shareware

Price

 288 USD

Category

 Home & Education / Mathematics

Operating System

 Win95,Win98,WinME,WinNT 4.x,Windows2000,WinXP,Windows2003

Developer

 CPC-X Software

 

Auto2Fit 3.0 Review




Auto2Fit is a revolution tools and beats all other simliar ones in the area of nonlinear regression!
Almost all data analysis software packages (SPSS, SAS, Statistical,Origin Pro, DataFit, Stata or Systat) need end-users to provide/guess initial start values, and the successes of nonlinear regression computation are havily depended on those guessed data. Unfortunately, for most of people, the guesses of proper start values are nightmare.
With the unique, robust and high efficient optimation algorithms of "Global Levenberg-Marquardt" and "Global BFGS", proposed newly, Auto2Fit is no long to require the end-users to provide/guess initial start values (but with free random ones), the performance, however, is much better than any others. Auto2Fit has turned to be the best one for nonlinear regerssion analysis nowadays.
Auto2Fit is also a flexible, versatile, easiest-to-use tool in the areas of function optimization, equation solving, parameter estimation, combinational problems (like TSP) and chart drawing.

Algorithms:
A number of optimization algorithms have been integrated in Auto2Fit:
1. Genetic Algorithm (GA): both encode/decode mode and real-value based mode, six crossover types and seven selection types
2. Particle Swarm Optimization (PSO): six schemes (2 standard and 4 new)
3. Differential Evolution (DE): ten schemes (5 new) and four routs (3 new)
4. Maximum Inherit Optimization (MIO): four schemes
5. Simulated Annealing (SA)
6. Simplex Method (SM)
7. Levenberg-Marquardt (LM): standard LM and Global LM
8. Quasi-Newton (BFGS): standard BFGS and Global BFGS
9. Self-Organizing Migrating Algorithms (SOMA)
10. Conjugate-Gradient Method (CGM)
11. Powell Optimization (OP)
12 Tabu Search (TS)
Program system requirements: 10 MB HD, 64MB RAM

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nonlinear regression, global optimization, genetic algorithms, parameter estimation, curve fit,

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