Full docs and demo

Below, we provide a brief description and demonstration of the capabilities of each function from the package. Most functions take “Y” - a list of the HT-cores for each level.

Please, note that all demos assume the following imports:

from jax.config import config            # Optional
config.update('jax_enable_x64', True)    # Optional
import os                                # Optional
os.environ['JAX_PLATFORM_NAME'] = 'cpu'  # Optional

import jax
import jax.numpy as jnp
import numpy as np
import teneva_ht_jax as tnv
from time import perf_counter as tpc
rng = jax.random.PRNGKey(42)

---

• Module act_one: single HT-tensor operations
  • get()
• Module gd: gradient descent for HT-tensors
  • gd_appr()
• Module tensors: collection of explicit useful HT-tensors
  • rand()
• Module transformation: orthogonalization, truncation and other transformations of the HT-tensors
  • full()
• Module vis: visualization methods for tensors
  • show()