Source code for cvxpy.atoms.norm

Copyright 2013 Steven Diamond

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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import numpy as np
import cvxpy
from cvxpy.expressions.expression import Expression
from cvxpy.atoms.norm_nuc import normNuc
from cvxpy.atoms.sigma_max import sigma_max
from cvxpy.atoms.pnorm import pnorm
from cvxpy.atoms.norm1 import norm1
from cvxpy.atoms.norm_inf import norm_inf
from cvxpy.atoms.affine.vec import vec

[docs]def norm(x, p=2, axis=None): """Wrapper on the different norm atoms. Parameters ---------- x : Expression or numeric constant The value to take the norm of. p : int or str, optional The type of norm. Returns ------- Expression An Expression representing the norm. """ x = Expression.cast_to_const(x) # matrix norms take precedence num_nontrivial_idxs = sum([d > 1 for d in x.shape]) if axis is None and x.ndim == 2: if p == 1: # matrix 1-norm return cvxpy.atoms.max(norm1(x, axis=0)) # Frobenius norm elif p == 'fro' or (p == 2 and num_nontrivial_idxs == 1): return pnorm(vec(x), 2) elif p == 2: # matrix 2-norm is largest singular value return sigma_max(x) elif p == 'nuc': # the nuclear norm (sum of singular values) return normNuc(x) elif p in [np.inf, "inf", "Inf"]: # the matrix infinity-norm return cvxpy.atoms.max(norm1(x, axis=1)) else: raise RuntimeError('Unsupported matrix norm.') else: if p == 1 or x.is_scalar(): return norm1(x, axis=axis) elif p in [np.inf, "inf", "Inf"]: return norm_inf(x, axis) else: return pnorm(x, p, axis)