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Survey of unconstrained optimization gradient based algorithms

Survey of unconstrained optimization gradient based algorithms. Unconstrained minimization Steepest descent vs. conjugate gradients Newton and quasi-Newton methods Matlab fminunc. Unconstrained local minimization. The necessity for one dimensional searches

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Survey of unconstrained optimization gradient based algorithms

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  1. Survey of unconstrained optimization gradient based algorithms • Unconstrained minimization • Steepest descent vs. conjugate gradients • Newton and quasi-Newton methods • Matlabfminunc

  2. Unconstrained local minimization • The necessity for one dimensional searches • The most intuitive choice of skis the direction of steepest descent • This choice, however is very poor • Methods are based on the dictum that all functions of interest are locally quadratic

  3. Conjugate gradients

  4. Newton and quasi-Newton methods • Newton • Quasi-Newton methods use successive evaluations of gradients to obtain approximation to Hessian or its inverse • Matlab’sfminunc uses a variant of Newton if gradient routine is provided, otherwise BFGS quasi-Newton. • The variant of Newton is called trust region approach and is based on using a quadratic approximation of the function inside a box.

  5. Problems Unconstrained algorithms • Explain the differences and commonalities of steepest descent, conjugate gradients, Newton’s method, and quasi-Newton methods for unconstrained minimization. • Use fminunc to minimize the Rosenbrock Banana function and compare the trajectories of fminsearch and fminunc starting from (-1.2,1), with and without the routine for calculating the gradient. Plot the three trajectories.

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