pyepo.data.tsp

Synthetic data for traveling salesman problem

Functions

genData(→ tuple[numpy.ndarray, numpy.ndarray])

Generate synthetic feature-cost pairs for the traveling salesperson problem.

Module Contents

pyepo.data.tsp.genData(num_data: int, num_features: int, num_nodes: int, deg: int = 1, noise_width: float = 0, seed: int = 135) → tuple[numpy.ndarray, numpy.ndarray]

Generate synthetic feature-cost pairs for the traveling salesperson problem.

Edge costs combine a Euclidean component (node coordinates drawn from a mixture of a Gaussian \(\mathcal{N}(0, \mathbf{I})\) and a uniform \(\mathbf{U}(-2, 2)\) distribution) with a feature-encoded component obtained by mapping the standard-Gaussian feature vector through a random Bernoulli \(\times\) uniform matrix \(\mathcal{B}\) and a polynomial of degree deg, scaled by multiplicative noise of half-width noise_width.

Parameters:

  • num_data – number of data points

  • num_features – dimension of features

  • num_nodes – number of nodes

  • deg – polynomial degree of the feature-to-cost mapping

  • noise_width – half-width of the multiplicative uniform noise

  • seed – random seed (default 135 for reproducibility)

Returns:

data features (np.ndarray), costs (np.ndarray)

Return type:

tuple