Projection onto the simplex

projection_simplex.py

"""
License: BSD
Author: Mathieu Blondel

Implements three algorithms for projecting a vector onto the simplex: sort, pivot and bisection.

For details and references, see the following paper:

Large-scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex
Mathieu Blondel, Akinori Fujino, and Naonori Ueda.
ICPR 2014.
http://www.mblondel.org/publications/mblondel-icpr2014.pdf

"""

import numpy as np


def projection_simplex_sort(v, z=1):
    n_features = v.shape[0]
    u = np.sort(v)[::-1]
    cssv = np.cumsum(u) - z
    ind = np.arange(n_features) + 1
    cond = u - cssv / ind > 0
    rho = ind[cond][-1]
    theta = cssv[cond][-1] / float(rho)
    w = np.maximum(v - theta, 0)
    return w


def projection_simplex_pivot(v, z=1, random_state=None):
    rs = np.random.RandomState(random_state)
    n_features = len(v)
    U = np.arange(n_features)
    s = 0
    rho = 0
    while len(U) > 0:
        G = []
        L = []
        k = U[rs.randint(0, len(U))]
        ds = v[k]
        for j in U:
            if v[j] >= v[k]:
                if j != k:
                    ds += v[j]
                    G.append(j)
            elif v[j] < v[k]:
                L.append(j)
        drho = len(G) + 1
        if s + ds - (rho + drho) * v[k] < z:
            s += ds
            rho += drho
            U = L
        else:
            U = G
    theta = (s - z) / float(rho)
    return np.maximum(v - theta, 0)


def projection_simplex_bisection(v, z=1, tau=0.0001, max_iter=1000):
    func = lambda x: np.sum(np.maximum(v - x, 0)) - z
    lower = np.min(v) - z / len(v)
    upper = np.max(v)

    for it in range(max_iter):
        midpoint = (upper + lower) / 2.0
        value = func(midpoint)

        if abs(value) <= tau:
            break

        if value <= 0:
            upper = midpoint
        else:
            lower = midpoint

    return np.maximum(v - midpoint, 0)


if __name__ == '__main__':
    v = np.array([1.1, 0.2, 0.2])
    z = 2

    w = projection_simplex_sort(v, z)
    print(np.sum(w))

    w = projection_simplex_pivot(v, z)
    print(np.sum(w))

    w = projection_simplex_bisection(v, z)
    print(np.sum(w))

Thanks @joseortiz3. I fixed the bug (there was a mistake in the bracketing interval).

In case someone needs to project each column of a 2D array onto the simplex, here is an adapted code sample (Python 3.9.5):

    def projection_simplex_sort_2d(v, z=1):
        """v array of shape (n_features, n_samples)."""
        p, n = v.shape
        u = np.sort(v, axis=0)[::-1, ...]
        pi = np.cumsum(u, axis=0) - z
        ind = (np.arange(p) + 1).reshape(-1, 1)
        mask = (u - pi / ind) > 0
        rho = p - 1 - np.argmax(mask[::-1, ...], axis=0)
        theta = pi[tuple([rho, np.arange(n)])] / (rho + 1)
        w = np.maximum(v - theta, 0)
        return w

Hehe, I also wrote a vectorized version here https://gist.github.com/mblondel/c99e575a5207c76a99d714e8c6e08e89