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Numerical Optimization ( steepest descent method)

  1. Apr 7, 2010 #1
    1. The problem statement, all variables and given/known data

    Consider the steepest descent method with exact line searches applied to the
    convex quadratic function f(x) = 1/2 xT Qx − bT x, ( T stands for transpose). show that if the initial point is such that x0 − x* ( x* is the exact solution of Qx = b) is parallel to an eigenvector of Q, then the steepest descentmethod will find the solution in one step.

    2. Relevant equations



    3. The attempt at a solution.

    I tried to find a relation between the eigenvector and the given initial point but I couldn't
     
  2. jcsd
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