COR-OPT / GeomStepDecay

GeomStepDecay

This repository contains a reference implementation for the Algorithms appearing in the paper [1] for a selection of statistical recovery problems, namely robust phase retrieval and blind deconvolution.

Dependencies

The code has been tested in Julia 1.0.2 and depends on a number of Julia packages. For the core implementation, found under src/:

• Distributions.jl: for generating streaming versions of phase retrieval / blind deconvolution with arbitrary measurement vectors.
• Polynomials.jl: for solving the proximal subproblems in robust blind deconvolution, which reduce to finding the roots of a quartic polynomial.
• MLDatasets: for obtaining the MNIST dataset, for the sparse logistic regression problem.

For the remaining scripts which are aimed to reproduce some of the experimental results found in the paper and can be found in the root directory of this repo, the following packages are required:

• ArgParse.jl: for providing an argparse-like prompt for the command line.
• PyPlot: for access to the Matplotlib backend for plotting. Please refer to the installation instructions of PyPlot for details.
• JLD: for loading the solution found by full proximal gradient for the MNIST experiment.

Quick Tour

We offer implementations for both the fixed and high probability variants of our algorithms for robust phase retrieval and blind deconvolution.

The first step is to include the two libraries:

include("src/RobustPR.jl")   # for phase retrieval
include("src/RobustBD.jl")   # for blind deconvolution

The user can generate problem instances under both finite-sample and streaming settings. For the former, measurement vectors are assumed Gaussian. Let us generate such a problem with dimension d = 100, number of measurements m = 8 * d and 15% of the entries corrupted with noise:

d = 100
probPR = RobustPR.genProb(8 * d, d, 0.15)  # 15% corrupted measurements
probBD = RobustBD.genProb(8 * d, d, 0.15)  # similarly

In the streaming setting, the user can pass arbitrary distributions which satisfy the interface designated by Distributions.jl. Below, we generate a blind deconvolution instance where the left measurement vector is sampled from a normal distribution and the right measurement vector is sampled from a truncated normal distribution in the range [-5, 5].

using Distributions

Ldist = Normal(0, 1); Rdist = TruncatedNormal(0, 1, -5, 5)
probBDStream = RobustBD.genProb(Ldist, Rdist, d)  # no noise by default

We can now proceed to solving the above problems. Both libraries expose two generic optimization methods. We will look at blind deconvolution:

RobustBD.opt(prob, δ, K, T, λ; method)
RobustBD.sOpt(prob, δ, ρ0, α0, K, ε0, M, T; method)

The method opt is the constant probability variant, while the method sOpt is the high-probability variant of the proposed algorithms. In the above, T is the number of "outer" loops, K is the number of "inner" loops, and δ is a specified initial distance from the solution set. The parameter λ can be either a callable accepting a single argument corresponding to the iteration counter, or a constant. For the rest of the arguments appearing above, please refer to Section 3 of [1]. Finally, method can be one of :subgradient, :proximal, :proxlinear, :clipped, corresponding to different local models.

We demonstrate solving one of the above problems using the prox-linear method with exponential step decay and initial step size of .01:

λSched = k -> 0.01 * 2.0^(-k)  # callable implementing step schedule
(wSol, xSol), ds, totalEv =
	RobustBD.opt(probBDStream, 0.1, 5000, 15, λSched, method=:proxlinear)

In the above, wSol and xSol are the final iterates found by the algorithm, ds is a history of distances from the solution set (one for each outer loop) and totalEv is the total number of inner iterations performed.

Running the MNIST experiment

In order to reproduce the sparse logistic regression experiment on the MNIST dataset, we offer the script plot_mnist.jl together with a JLD file mnist_full.jld. The latter contains the approximate solution found by running the full proximal gradient method. To evaluate the performance of the RMBA method, make sure mnist_full.jld is in the same directory as plot_mnist.jl and run

julia -O3 plot_mnist.jl plot_rmba

To evaluate the RDA algorithm with γ = 0.1, (see [2] for details), type:

julia -O3 plot_mnist.jl plot_rda --gamma 0.1

[1]: Damek Davis, Dmitriy Drusvyatskiy, Vasileios Charisopoulos. Stochastic Algorithms with geometric step decay converge linearly on sharp functions. Available here.

[2]: Lee, Sangkyun, and Stephen J. Wright. Manifold identification in dual averaging for regularized stochastic online learning. Journal of Machine Learning Research 13.Jun (2012): 1705-1744.