conjugateGradientSearchDirection()

conjugateGradientSearchDirection.py

# ------------------------------------------------------------------------
# The following function is implemented in Python by Professor Terje Haukaas
# at the University of British Columbia in Vancouver, Canada. It is made
# freely available online at terje.civil.ubc.ca together with notes,
# examples, and additional Python code. Please be cautious when using
# this code; it may contain bugs and comes without any form of warranty.
# ------------------------------------------------------------------------

def conjugateGradientSearchDirection(gradient, previousGradient):

    # Calculate the suared norm of the gradients
    thisGradientNormSquared = (np.linalg.norm(gradient))**2
    previousGradientNormSquared = (np.linalg.norm(previousGradient))**2

    steepest = np.multiply(gradient, -1.0)
    if previousGradientNormSquared == 0.0:

        return steepest

    else:
        return steepest + np.multiply(previousGradient, -thisGradientNormSquared / previousGradientNormSquared)