Search Results for "nelder mead"

...Tools to build deep probabilistic models, including probabilistic layers and a `JointDistribution` abstraction. Variational inference and Markov chain Monte Carlo. A wide selection of probability distributions and bijectors. Optimizers such as Nelder-Mead, BFGS, and SGLD.

Airfoil optimization using the highly-regarded Xfoil engine for aerodynamic calculations. Starting with a seed airfoil, Xoptfoil uses particle swarm, genetic algorithm and direct search methodologies to perturb the geometry and maximize performance. The user selects a number of operating points over which to optimize, desired constraints, and the optimizer does the rest.

OptimC is a C software package to minimize any unconstrained multivariable function. The algorithms implemented are Nelder-Mead,Newton Methods (Line Search and Trust Region methods), Conjugate Gradient and BFGS (regular and Limited Memory). Brent method is also available for single variable functions if the bounds are known. Update 06/09/2014 - Nonlinear Squares Implementation [Levenberg-Marquardt Method] Added. Documentation - http://code.google.com/p/optimc/

Keywords: -simultaneously fit several SAXS and SANS data sets with polydisperse (Schultz-Zimm or Gaussian distribution f(R)) spherical core-shell-shell nanoparticles -analytical expressions are used for from factor F(Q) and its integral over f(R), no numerical integration required -absolute units -Mathematica is required via console (MathKernel) -Mathematica's local and global optimizers (simulated annealing, differential evolution, Nelder-Mead, ...) can be used -range for fit parameters and further constraints between fit parameters are possible -Monodisperse(!) hard sphere structure factor can be used, too -long computation times (depending on problem size and amount of constraints) from hours to a few days are possible -non-parallelized code

Keywords (XNDiff): -SAXS -SANS -absolute units -core (double)shell crystalline nanoparticles -with a parallelepidal shape -particle assemblies -powder and ensemble average -C/C++ -Unix -OpenMP -HPC Cluster Keywords (BatchMultiFit): -simultaneous fits for several SAXS and SANS curves with simulation data from XNDiff -SANS data can be smeared with dq values from experimental data sets or analytical functions -Mathematica console -local and global optimizers (simulated annealing, differential evolution, Nelder-Mead, ...) can be used -range for fit parameters and further constraints between fit parameters -parallelized (typ. 4-8 threads) TODO (BatchMultiFit): -read and use errorbars from experimental data sets -allow different q-ranges for different data sets in the fits -rewrite and test in Python using e.g. the lmfit module: https://pypi.python.org/pypi/lmfit/ to get rid of Mathematica and to run it on HPC clusters