p103_7P151-4

p103_7P151-4 - EE103 Spring 2010 Lecture Notes (SEJ)...

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EE103 Spring 2010 Lecture Notes (SEJ) Section 7 151 Summary of Optimization Methods Steepest Descent Algorithm (general idea) Select an initial vector n x R DoWhile (some appropriate stopping condition has not occurred) Compute () gx and the gradient vector If () 0  T dg x  else return; end Select such that ( ) ( ) d  (this step not explained in this pseudo-code) x xd  End DoWhile Newton Optimization Algorithm (general idea) Select an initial vector n x R DoWhile (some appropriate stopping condition has not occurred) Compute , , g gx H x If  return; end If g Hx is PD 1 T g dH g x  (not to be computed by computing the inverse) Select such that ( ) d (not explained in this pseudo-code) x else (a heuristic if not PD) T x Select such that ( ) ( ) d (not explained in this pseudo-code) x end End DoWhile Note: In order to determine if g H is PD, we may implement Matlab’s command:  [ , ] L pd chol H to determine if g H is PD; if 0 pd , then g H is not PD.
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EE103 Spring 2010 Lecture Notes (SEJ) Section 7 152 Tutorial Codes (i.e., for classroom purposes)
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This note was uploaded on 06/01/2010 for the course EE EE 103 taught by Professor Jacobsen during the Spring '09 term at UCLA.

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p103_7P151-4 - EE103 Spring 2010 Lecture Notes (SEJ)...

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