ACSL Optimize(ID:6492/acs004)

• Page at ICT ACSL Optimize gives the user robust parameter estimation, optimisation, and sensitivity analysis capabilities, that are critical to solving the most complex applied mathematical problems. Its parameter estimation module tests a non-linear model against experimental data or estimates model parameters that are difficult or impossible to measure directly.
  Parameter Estimation When running parameter estimation the user has complete control over the model. He is able to:
  
  Choose the parameter estimation method
  Select the model output variables to fit experimental data.
  Set the error model parameters or have ACSL Optimize find the most likely one
  Select the model input variables to be adjusted
  Set constraints on the adjustable parameters
  Identify the number of experiments to perform Optimisation ACSL. Optimize uses the two most robust optimisation methods: Nelder-Mead and Generalised Reduced Gradient.
  Sensitivity Analysis ACSL Optimise calculates sensitivity coefficients which are the partial derivatives of model responses with respect to model parameters. ACSL Optimize implements two methods of calculating sensitivity coefficients: the direct method and the finite difference method.