Source code for cvxpy.atoms.lambda_sum_largest

Copyright 2013 Steven Diamond

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from scipy import linalg as LA
from cvxpy.atoms.lambda_max import lambda_max
from cvxpy.atoms.sum_largest import sum_largest

[docs]class lambda_sum_largest(lambda_max): """Sum of the largest k eigenvalues. """ _allow_complex = True def __init__(self, X, k): self.k = k super(lambda_sum_largest, self).__init__(X) def validate_arguments(self): """Verify that the argument A is square. """ X = self.args[0] if not X.ndim == 2 or X.shape[0] != X.shape[1]: raise ValueError("First argument must be a square matrix.") elif int(self.k) != self.k or self.k <= 0: raise ValueError("Second argument must be a positive integer.") def numeric(self, values): """Returns the largest eigenvalue of A. Requires that A be symmetric. """ eigs = LA.eigvals(values[0]) return sum_largest(eigs, self.k).value def get_data(self): """Returns the parameter k. """ return [self.k] def _grad(self, values): """Gives the (sub/super)gradient of the atom w.r.t. each argument. Matrix expressions are vectorized, so the gradient is a matrix. Args: values: A list of numeric values for the arguments. Returns: A list of SciPy CSC sparse matrices or None. """ return NotImplemented