Lecture-4 - Lecture-4 Line Search Methods: Search...

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1 Lecture-4 Line Search Methods: Search Directions, and step lengths Line Search Methods k k k k p x x a + + 1 k k k f B p - - 1 Steepest descent is and identity matrix Newton is a Hessian matrix Quasi-Newton is approximation to the Hessian matrix k B k B k B
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2 k k k f H p - = 1 , ) ( ) ( 1 k T k k T k k k T k k k k T k k k k s y s s y s I H y s I H = + - - = + r r r r 1 - = k k B H Instead of inverting approximation of Hessian, we can directly compute the approximation of inverse of Hessian: Inverse Hessian Quasi Newton k k k k k f f x x s - = - = + + 1 k 1 y , Conjugate Gradient 1 ) ( - + -∇ = k k k k p x f p b is scalar such that and are conjugate k p 1 - k p 0 1 = - k T k Gp p Two vectors are conjugate with respect to a matrix G if k b Non-interfering directions, with the special property that minimization along one direction is not spoiled by subsequent minimization along another.
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3 Step Length 0 ) ( ) ( + = a a a f k k p x f ( Exact Search ) The global minimizer of the univariate function: Too many evaluations of a function, and its gradient ( In-exact search ): adequate reduction in f at minimal cost. Two step method:
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This note was uploaded on 06/12/2011 for the course COT 6505 taught by Professor Shah during the Spring '07 term at University of Central Florida.

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Lecture-4 - Lecture-4 Line Search Methods: Search...

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