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SCG convergence tolerance increased, will now converge more easily

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Max Zwiessele 2013-05-08 15:35:41 +01:00
parent ff863612eb
commit bc0bd59874
1 changed files with 3 additions and 2 deletions
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5
GPy/inference/SCG.py
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@ -54,7 +54,7 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
success = True # Force calculation of directional derivs.
nsuccess = 0 # nsuccess counts number of successes.
beta = 1.0 # Initial scale parameter.
betamin = 1.0e-15 # Lower bound on scale.
betamin = 1.0e-25 # Lower bound on scale.
betamax = 1.0e100 # Upper bound on scale.
status = "Not converged"
@ -116,7 +116,7 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
if success:
# Test for termination
if (np.dot(gradnew, gradnew)) <= gtol or (np.max(np.abs(alpha * d)) < xtol) or (np.abs(fnew - fold) < ftol):
if (np.max(np.abs(alpha * d)) < xtol) or (np.abs(fnew - fold) < ftol):
status = 'converged'
return x, flog, function_eval, status
@ -140,6 +140,7 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
# in direction of negative gradient after nparams steps.
if nsuccess == x.size:
d = -gradnew
# beta = 1. # TODO: betareset!!
nsuccess = 0
elif success:
gamma = np.dot(gradold - gradnew, gradnew) / (mu)