print('%s ' % ('-'*78))
print('Sample the Pareto frontier at 100 points: ')
print(' ')
print(' phi(tau) = minimize ||Ax-b||_2 subject to ||x|| <= tau')
print(' ')
print('%s%s ' % ('-'*78, '\n'))
print('Computing sample')
# % Set up vector b, and run solver
b = A.dot(x0)
x = np.zeros(n)
tau = np.linspace(0,1.05 * np.linalg.norm(x0,1),100)
phi = np.zeros(tau.size)
opts = spgSetParms({'iterations':1000})
for i in range(tau.size):
x,r,grad,info = spgl1(A,b,tau[i],0,x,opts)
phi[i] = np.linalg.norm(r)
if np.mod(i,10)==0:
print('...%i'%i)
figure()
plot(tau,phi)
title('(d) Pareto frontier')
xlabel('||x||_1')
ylabel('||Ax-b||_2')
print('%s%s%s' % ('-'*35,' Solution ','-'*35))
print('See figure 1(d)')
print('%s%s ' % ('-'*78, '\n'))
while (dmisAll.eval(mwav) / survey.nD) > 0.5 and it < nits:
problem.unpair()
problem.pair(survey_r)
def JS(x, mode):
if mode == 1:
return problem.Jvec(mwav, iwavmap * x)
else:
return wavmap * problem.Jtvec(mwav, x)
b = survey_r.dpred(mwav) - survey_r.dpred(mtrue)
print "# of data: ", b.shape
opts = spgl1.spgSetParms({'iterations': 100, 'verbosity': 2})
sigtol = np.linalg.norm(b) / np.maximum(ratio[it], min_progress)
#tautol = 20000.
tic = time.clock()
x, resid, grad, info = spgl1.spg_bpdn(JS, b, sigma=sigtol, options=opts)
toc = time.clock()
print 'SPGL1 chronometer: ', toc - tic
timespgl1.append(toc - tic)
#x,resid,grad,info = spgl1.spg_lasso(JS,b,tautol,opts)
#assert dmis.eval(mwav) > dmis.eval(mwav - iwavmap*x)
mwav = mwav - iwavmap * x
xlist.append(x)