class_docstr: Tests for LBFGS optimization algorithm.
class_docstr_tok: ["Tests","for","LBFGS","optimization","algorithm","."]
docstr: Can minimize a high-dimensional quadratic function.
docstr_tok: ["Can","minimize","a","high-dimensional","quadratic","function","."]
code: def test_quadratic_bowl_40d(self):
      """Can minimize a high-dimensional quadratic function."""
      dim = 40
      np.random.seed(14159)
      minimum = np.random.randn(dim)
      scales = np.exp(np.random.randn(dim))
  
      @_make_val_and_grad_fn
      def quadratic(x):
          return tf.reduce_sum(
              scales * tf.math.squared_difference(x, minimum)
          )
  
      start = tf.ones_like(minimum)
      results = self.evaluate(
          lbfgs.minimize(
              quadratic, initial_position=start, tolerance=1e-8
          )
      )
      self.assertTrue(results.converged)
      self.assertLessEqual(_norm(results.objective_gradient), 1e-8)
      self.assertArrayNear(results.position, minimum, 1e-5)
  
imports: from absl.testing import parameterized
  import numpy
  from scipy.stats import special_ortho_group
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.optimizer import bfgs_utils
  from tensorflow_probability.python.optimizer import lbfgs
label_desc: Use image node_id 4 for calling the LBfgsTest obj's underlying member method code with example usage: obj.test_quadratic_bowl_40d() and returns: tf