CompOpt-LowRankMatrixRecovery

Implementation of nonsmooth optimization algorithms for matrix recovery problems.

Subgradient and prox-linear methods as well as methods for generating synthetic instances of matrix sensing, robust PCA and matrix completion instances are available under src/CompOpt.jl.

Dependencies

The scripts depend on ArgParse and PyPlot. The core library has no external dependencies.

Quick Tour

The matrix recovery problems covered are bilinear and quadratic sensing, matrix completion, and robust PCA. We provide generators for synthetic instances of all these problems, as demonstrated below:

include("src/CompOpt.jl");

prob_bilin = CompOpt.genBilinProb(50, 100, 5, 0.25)
prob_quad = CompOpt.genSymQuadProb(50, 5, 0.25)
prob_rpca = CompOpt.genRpcaProb(50, 5, 0.25)
prob_mcomp = CompOpt.genMatCompProb(50, 5, 0.25)

In all but the first constructors above, the first argument is the dimension d (number of rows of the matrix), second argument is the rank r, and third argument is either the level of corruption with arbitrary noise (in the case of matrix sensing and robust PCA) or the fraction of observed entries (in matrix completion). In the bilinear sensing constructor, the first two arguments are devoted to sizes of different matrices, since the signals might have different sizes.

The above constructors are not the only way to generate problem instances. You can define your own signals and corruptions. Below we give an example of a 100 x 5 quadratic sensing instance with a total of 1000 measurements, the first 100 of which are corrupted by replacing them with a value chosen in {-1, 1} at random.

using LinearAlgebra
using Random
include("src/CompOpt.jl")

# generate data
d = 100; r = 5; m = 1000; pfail = 0.1
A = randn(m, d); X = randn(d, r)
y = mapslices(x -> norm(x)^2, A * X, dims=[2])[:]  # cast to vector

# corrupt measurements
num_corrupted = trunc(Int, pfail * m)
y[1:num_corrupted] = rand([-1, 1], num_corrupted)

prob = CompOpt.QuadProb(y, X, A, pfail)

We have implemented 2 composite optimization methods - subgradient and prox-linear - which are available as the functions CompOpt.pSgd_init and CompOpt.proxlin_init, respectively. They accept a problem instance, the number of desired iterations and a parameter delta which produces an initial estimate delta-close to the ground truth (in normalized distance). The functions implementing the prox-linear method accept extra arguments controlling the way proximal subproblems are solved, and therefore vary slightly across problem types, since we provide theoretical guarantees for modified versions of the prox-linear method for matrix completion and robust PCA. The user is invited to look at the documentation for a more complete overview.

See an example below, which applies the prox-linear method to a matrix completion problem starting from an estimate with normalized distance 1.5 from the solution set.

prob = CompOpt.genMatCompProb(100, 5, 0.25);
Xest, ds = CompOpt.proxlin_init(prob, 15, 1.5)

In the above, Xest contains the final estimate after 15 iterations, and ds contains the normalized error across all iterates.