tau = rho / norm_K
f = KL
A = K
pdhg(x, f, g, A, tau, sigma, niter, callback=cb)
elif alg.startswith('SPDHG1'):
norm_K = misc.norms(
Ks, '{}/norm_{}subsets.npy'.format(folder_norms,
nsub[alg]))
sigma = [rho / nk for nk in norm_K]
tau = rho / (len(Ks) * max(norm_K))
f = KLs
A = Ks
spdhg(x, f, g, A, tau, sigma, niter, callback=cb)
elif alg.startswith('PDHG2'):
f = KL
A = K
one = A.domain.one()
tmp = A.range.element()
A(one, out=tmp)
tmp.ufuncs.maximum(tol_step, out=tmp)
sigma = rho / tmp
one = A.range.one()
tmp = A.domain.element()
A.adjoint(one, out=tmp)
tmp.ufuncs.maximum(tol_step, out=tmp)
assert False, "Parameters not defined"
# function that selects the indices every iteration
def fun_select(k):
return sub2ind[int(np.random.choice(n, 1, p=prob_subset))]
# output function to be used within the iterations
callback = (odl.solvers.CallbackPrintIteration(step=n, end=', ') &
odl.solvers.CallbackPrintTiming(step=n, cumulative=True) &
CallbackStore(alg, iter_save[alg], iter_plot[alg]))
x, y = X.zero(), Y.zero() # initialise variables
callback([x, y])
if alg.startswith('pdhg') or alg.startswith('spdhg'):
spdhg(x, f, g, A, tau, sigma, niter[alg], prob=prob, y=y,
fun_select=fun_select, callback=callback)
elif alg.startswith('pa_pdhg') or alg.startswith('pa_spdhg'):
pa_spdhg(x, f, g, A, tau, sigma, niter[alg], mu_g, prob=prob, y=y,
fun_select=fun_select, callback=callback)
elif alg.startswith('pesquet'):
spdhg_pesquet(x, f, g, A, tau, sigma, niter[alg], y=y,
fun_select=fun_select, callback=callback)
else:
assert False, "Algorithm not defined"
np.save('{}/{}_output'.format(folder_npy, alg), (iter_save[alg],
niter[alg], x, callback.callbacks[1].out, nsub[alg]),
allow_pickle=True)
norm_A = misc.norm(A, '{}/norm_tv.npy'.format(folder_main))
sigma = rho / norm_A
tau = rho / norm_A
pdhg(x, f, g, A, tau, sigma, niter, callback=cb)
elif alg.startswith('SPDHG1'):
sigma = [rho / nAi for nAi in norm_Ai]
tau = rho * min(
[pi / nAi for pi, nAi in zip(prob, norm_Ai)])
spdhg(x,
f,
g,
A,
tau,
sigma,
niter,
prob=prob,
callback=cb)
elif alg.startswith('PDHG2'):
one = A[0].domain.one()
tmp = A[0].range.element()
A[0](one, out=tmp)
tmp.ufuncs.maximum(tol_step, out=tmp)
sigma = [rho / tmp, rho / norm_D]
one = A[0].range.one()
tmp = A[0].domain.element()
A[0].adjoint(one, out=tmp)
