#!/usr/bin/env python
# coding: utf-8

# # Rank-one nonnegative matrix factorization
# 
# The DGP atom library has several functions of positive matrices, including the trace, (matrix) product, sum, Perron-Frobenius eigenvalue, and $(I - X)^{-1}$ (eye-minus-inverse). In this notebook, we use some of these atoms to approximate a partially known elementwise positive matrix
# as the outer product of two positive vectors.
# 
# We would like to approximate $A$ as the outer product of two positive vectors $x$ and $y$, with $x$ normalized so that the product of its entries equals $1$. Our criterion is the average relative deviation between the entries of $A$ and
# $xy^T$, that is,
# 
# $$
# \frac{1}{mn} \sum_{i=1}^{m} \sum_{j=1}^{n} R(A_{ij}, x_iy_j),
# $$
# 
# where $R$ is the relative deviation of two positive numbers, defined as
# 
# $$
# R(a, b) = \max\{a/b, b/a\} - 1.
# $$
# 
# The corresponding optimization problem is
# 
# $$
# \begin{equation}
# \begin{array}{ll}
# \mbox{minimize} & \frac{1}{mn} \sum_{i=1}^{m} \sum_{j=1}^{n} R(X_{ij}, x_iy_j)
# \\
# \mbox{subject to} & x_1x_2 \cdots x_m = 1 \\
# & X_{ij} = A_{ij}, \quad \text{for } (i, j) \in \Omega,
# \end{array}
# \end{equation}
# $$
# 
# with variables $X \in \mathbf{R}^{m \times n}_{++}$, $x \in \mathbf{R}^{m}_{++}$, and $y \in \mathbf{R}^{n}_{++}$. We can cast this problem as an equivalent generalized geometric program by discarding the $-1$ from the relative deviations.
# 
# The below code constructs and solves this optimization problem, with specific problem data
# 
# $$
# A = \begin{bmatrix}
# 1.0 & ? &  1.9 \\
# ? & 0.8 &  ? \\
# 3.2 & 5.9&  ?
# \end{bmatrix},
# $$

# In[1]:


import cvxpy as cp

m = 3
n = 3
X = cp.Variable((m, n), pos=True)
x = cp.Variable((m,), pos=True)
y = cp.Variable((n,), pos=True)

outer_product = cp.vstack([x[i] * y for i in range(m)])
relative_deviations = cp.maximum(
  cp.multiply(X, outer_product ** -1),
  cp.multiply(X ** -1, outer_product))
objective = cp.sum(relative_deviations)
constraints = [
  X[0, 0] == 1.0,
  X[0, 2] == 1.9,
  X[1, 1] == 0.8,
  X[2, 0] == 3.2,
  X[2, 1] == 5.9,
  x[0] * x[1] * x[2] == 1.0,
]
problem = cp.Problem(cp.Minimize(objective), constraints)
problem.solve(gp=True)

print("Optimal value:\n", 1.0/(m * n) * (problem.value - m * n), "\n")
print("Outer product approximation\n", outer_product.value, "\n")
print("x: ", x.value)
print("y: ", y.value)