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tf.contrib.constrained_optimization.find_best_candidate_index

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Heuristically finds the best candidate solution to a constrained problem.

tf.contrib.constrained_optimization.find_best_candidate_index(
    objective_vector,
    constraints_matrix,
    rank_objectives=False
)

This function deals with the constrained problem:

minimize f(w) s.t. g_i(w) <= 0 for all i in {0,1,...,m-1}

Here, f(w) is the "objective function", and g_i(w) is the ith (of m) "constraint function". Given a set of n "candidate solutions" {w_0,w1,...,w{n-1}}, this function finds the "best" solution according to the following heuristic:

  1. Across all models, the ith constraint violations (i.e. max{0, g_i(0)}) are ranked, as are the objectives (if rank_objectives=True).
  2. Each model is then associated its MAXIMUM rank across all m constraints (and the objective, if rank_objectives=True).
  3. The model with the minimal maximum rank is then identified. Ties are broken using the objective function value.
  4. The index of this "best" model is returned.

The objective_vector parameter should be a numpy array with shape (n,), for which objective_vector[i] = f(w_i). Likewise, constraints_matrix should be a numpy array with shape (m,n), for which constraints_matrix[i,j] = g_i(w_j).

For more specifics, please refer to:

Cotter, Jiang and Sridharan. "Two-Player Games for Efficient Non-Convex Constrained Optimization". https://arxiv.org/abs/1804.06500

This function implements the heuristic used for hyperparameter search in the experiments of Section 5.2.

Args:

  • objective_vector: numpy array of shape (n,), where n is the number of "candidate solutions". Contains the objective function values.
  • constraints_matrix: numpy array of shape (m,n), where m is the number of constraints and n is the number of "candidate solutions". Contains the constraint violation magnitudes.
  • rank_objectives: bool, whether the objective function values should be included in the initial ranking step. If True, both the objective and constraints will be ranked. If False, only the constraints will be ranked. In either case, the objective function values will be used for tiebreaking.

Returns:

The index (in {0,1,...,n-1}) of the "best" model according to the above heuristic.

Raises:

  • ValueError: If objective_vector and constraints_matrix have inconsistent shapes.
  • ImportError: If we're unable to import scipy.stats.