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Python implementation for optimization with Low-Rank Inducing Norms

Project description

LRIPy

Python code for Low-rank optimization by Low-Rank Inducing Norms as well as non-convex Douglas-Rachford.

Purpose:

Low-rank rank inducing norms and non-convex Proximal Splitting Algoriths attempt to find exact rank/cardinality-r solutions to minimization problems with convex loss functions, i.e., avoiding of regularzation heuristics. LRIPy provides Python implementations for the proximal mappings of the low-rank inducing Frobenius and Spectral norms, as well as, their epi-graph projections and non-convex counter parts.

Literature:

Low-rank inducing norms:

Non-convex counter parts:

Installation

The easiest way to install the package is to run pip install lripy. To install the package from source, run python setup.py install in the main folder.

Documentation

In the following it holds that

  • for the low-rank inducing Frobenius norm: p = 2
  • for the low-rank inducing Spectral norm: p = 'inf'

Examples

There are two examples in the "example" folder:

  1. Exact Matrix Completion
  2. Low-rank approximation with Hankel constraint

Optimization

LRIPy contains Douglas-Rachford splitting implementations for "Exact Matrix Completion" and "Low-rank Hankel Approximation", both with low-rank inducing norms, as well as, non-convex Douglas-Rachford splitting. It is easy to modify these functions for other constraints!

Exact Matrix completion

Let N be a matrix and Index be a binary matrix of the same size, where the ones indicate the known entries N. We attempt to find a rank-r completion M:

# Import the Douglas-Rachford Completion function:

from lripy import drcomplete

# Low-rank inducing norms with Douglas-Rachford splitting:

M = drcomplete(N,Index,r,p)[0]

# Non-convex Douglas-Rachford splitting:

M = drcomplete(N,Index,r,p,solver = 'NDR')[0]

Low-rank Hankel Approximation

Let H be a matrix. We attempt to find a rank-r Hankel approximation M that minimizes the Frobenius norm:

# Import the Douglas-Rachford Hankel Approximation function:

from lripy import drhankelapprox

# Low-rank inducing norms with Douglas-Rachford splitting:

M = drhankelapprox(H,r)[0]

# Non-convex Douglas-Rachford splitting:

M = drhankelapprox(H,r,solver = 'NDR')[0]

Proximal Mappings

LRIPy provides Python implemenations for the proximal mappings to the low-rank inducing Frobenius and Spectral norm as well as their epi-graph projections and non-convex counter parts.

Low-rank inducing Spectral and Frobenius norms:

Proximal mapping of the low-rank inducing norms at Z with parameter r and scaling factor gamma:

X = proxnormrast(Z,r,p,gamma)[0]

Squared Low-rank inducing Spectral and Frobenius norms:

Proximal mapping of the SQUARED low-rank inducing norms at Z with parameter r and scaling factor gamma:

X = proxnormrast_square(Z,r,p,gamma)[0]

Projection onto the epi-graph of the low-rank inducing norms:

Projection of (Z,zv) on the epi-graph of the low-rank inducing norms with parameter r and scaling factor gamma:

X,xv = projrast(Z,zv,r,p,gamma)[0:2]

Non-convex proximal mappings for Frobenius and Spectral norm:

Non-convex proximal mapping of at Z with parameter r and scaling factor gamma:

X = proxnonconv(Z,r,p,gamma)

Non-convex proximal mappings for squared Frobenius and Spectral norm:

Non-convex proximal mapping for the SQUARED norms at Z with parameter r and scaling factor gamma:

X = proxnonconv_square(Z,r,p,gamma)

Project details


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