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Jun 5

Matlab optimization introduction

Posted by abiao at 09:12 | Code » Matlab | Comments(0) | Reads(10160)
it is indisputable that optimization has been a crucial part to our financial world, the application of optimization routine ranges from fundamental mean-variance Markowitz efficient frountier to advanced neural network stock price prediction. Here is a carefully selected group of methods for unconstrained and bound constrained Matlab optimization problems including:
Line Search Methods:
steep.m : Steepest Descent
gaussn.m : Damped Gauss-Newton
bfgswopt.m : BFGS, low storage
Polynomial line search routines: polyline.m , polymod.m
Numerical Derivatives: diffhess.m : Difference Hessian,
requires dirdero.m : directional derivative, as do several other codes
Trust Region Codes:
ntrust.m : Newton's Method with Simple Dogleg
levmar.m : Levenberg-Marquardt for nonlinear least squares
cgtrust.m : Steihaug CG-dogleg
Bound Constrained Problems:
gradproj.m : Gradient Projection Method
projbfgs.m: Projected BFGS code
Noisy Problems:
imfil.m : Implicit Filtering
nelder.m : Nelder-Mead
simpgrad.m : Simplex Gradient, used in implicit filtering and Nelder-Mead codes
hooke.m : Hooke-Jeeves code
mds.m : Multidirectional Search code  
Check http://www.siam.org/books/kelley/fr18/matlabcode.php for detail.

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