Source code for cvxpy.atoms.total_variation

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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from cvxpy.expressions.expression import Expression
from cvxpy.atoms.norm import norm
from cvxpy.atoms.affine.vstack import vstack
from cvxpy.atoms.affine.sum import sum
from cvxpy.atoms.affine.reshape import reshape

[docs]def tv(value, *args): """Total variation of a vector, matrix, or list of matrices. Uses L1 norm of discrete gradients for vectors and L2 norm of discrete gradients for matrices. Parameters ---------- value : Expression or numeric constant The value to take the total variation of. args : Matrix constants/expressions Additional matrices extending the third dimension of value. Returns ------- Expression An Expression representing the total variation. """ value = Expression.cast_to_const(value) if value.ndim == 0: raise ValueError("tv cannot take a scalar argument.") # L1 norm for vectors. elif value.ndim == 1: return norm(value[1:] - value[0:value.shape[0]-1], 1) # L2 norm for matrices. else: rows, cols = value.shape args = map(Expression.cast_to_const, args) values = [value] + list(args) diffs = [] for mat in values: diffs += [ mat[0:rows-1, 1:cols] - mat[0:rows-1, 0:cols-1], mat[1:rows, 0:cols-1] - mat[0:rows-1, 0:cols-1], ] length = diffs[0].shape[0]*diffs[1].shape[1] stacked = vstack([reshape(diff, (1, length)) for diff in diffs]) return sum(norm(stacked, p=2, axis=0))