Pymanopt stiefel #4
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…into pymanopt # Conflicts: # tfimps.py
…into entangle # Conflicts: # test_tfimps.py # tfimps.py
…nce is better with Cojugate-Gradient than with Steepest descent. In the end, there were no difficulties with tf.while_loop, it stays as it was. I only needed to include the Stiefel variable to fix the dimension mismatch.
…nce is better with Cojugate-Gradient than with Steepest descent. In the end, there were no difficulties with tf.while_loop, it stays as it was. I only needed to include the Stiefel variable to fix the dimension mismatch. Is there any reason why you define learning_rate? It seems to be not doing anything.
2) I introduced r_prec which sets the condition of convergence the right eigenvector 3) I changed the definition of X1 for an equivalent simpler one. 4) I introduced two new methods to express the energy as a sum of an onsite and NN term (instead using the trick with identity that you use in the definition of X1). This gives the same result. But I am not using it, I sticked to the idenity trick in X1.
2) I introduced r_prec which sets the condition of convergence the right eigenvector 3) I changed the definition of X1 for an equivalent simpler one. 4) I introduced two new methods to express the energy as a sum of an onsite and NN term (instead using the trick with identity that you use in the definition of X1). This gives the same result. But I am not using it, I sticked to the idenity trick in X1.
2) I introduced r_prec which sets the condition of convergence the right eigenvector 3) I changed the definition of X1 for an equivalent simpler one. 4) I introduced two new methods to express the energy as a sum of an onsite and NN term (instead using the trick with identity that you use in the definition of X1). This gives the same result. But I am not using it, I sticked to the idenity trick in X1.
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All tests are failing for me at the moment. Seems to be because of this |
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What's the idea behind the altered convergence condition? I suppose it must have different scaling with |
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Probably should avoid |
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It seems to be working for h_Ising at criticality. Conjugate gradient works better than Steepest descent. In the end there were no problems with tf.while_loop. Why do define learning_rate? It seems to be doing nothing.