def scipy_slsqp_sparse(prb_map12_init, y_dm1, x_dm2, cs_idxs, max_nfe=50000, vis=False):
#test the gradient
from scipy.optimize import check_grad
err = check_grad(f_1D_slsqp_sparse, gradient_f_1D_slsqp_sparse, prb_map12_init.flatten(), y_dm1, x_dm2, cs_idxs) #correct
print 'Check grad: ', err, err1
stop
from scipy.optimize import minimize, fmin_slsqp
size1 = np.shape(y_dm1)[0]
size2 = np.shape(x_dm2)[0]
bnds = create_bounds_sparse(size1,size2, cs_idxs)
cons = create_constrains(size1)
#stop
t0 = cpu_time()
#bounds and constrains
#if vis:
# print 'Optimizing based on slsqp with bounds and constrains'
#res = minimize(f_1D_slsqp, prb_map12_init.flatten(),args=(y_dm1, x_dm2), method='SLSQP', bounds=bnds,
# constraints = cons, options = ({'maxiter' : max_nfe, 'disp': True}))
if vis:
print 'Optimizing based on slsqp with bounds and constrains. Also gradient is provided'
res = minimize(f_1D_slsqp_sparse, prb_map12_init.flatten(), args=(y_dm1, x_dm2, cs_idxs),
method='SLSQP',
jac = gradient_f_1D_slsqp_sparse,
bounds=bnds,
constraints = cons,
callback = inter_loss,
options = ({'maxiter' : max_nfe, 'disp': True}))
t_opt = cpu_time() - t0
plsq = res.x
prb_map12 = np.reshape(plsq,(size1,-1))
#print 'Number of iteration', res.nit
#print 'Final value of object func:', res.fun
#print 'Exit mode', res.status, res.message
if vis:
#print plsq
#print 'Probability mapping: ', prb_map12
begin_err = f_1D_slsqp(prb_map12_init.flatten(), y_dm1, x_dm2)
print 'Loss function before optimizing: ', begin_err
final_err = f_1D_slsqp(plsq, y_dm1, x_dm2)
print 'Loss function after optimizing: ', final_err
print 'Optimizing cpu time: ', t_opt
return t_opt, prb_map12
