Inertial Block Proximal Methods for Non-Convex Non-Smooth Optimization
Authors: Hien Le, Nicolas Gillis, Panagiotis Patrinos
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We deploy the proposed methods to solve non-negative matrix factorization (NMF) and show that they compete favourably with the state-of-the-art NMF algorithms. Additional experiments on non-negative approximate canonical polyadic decomposition, also known as non-negative tensor factorization, are also provided. |
| Researcher Affiliation | Academia | 1Department of Mathematics and Operational Research, University of Mons, Belgium 2Department of Electrical Engineering (ESAT-STADIUS), KU Leuven, Belgium. |
| Pseudocode | Yes | Algorithm 1 IBP and IBPG |
| Open Source Code | Yes | The code is available at https://github.com/Le Thi Khanh Hien/IBPG |
| Open Datasets | Yes | We test the algorithms on two widely used hyperspectral images, namely the Urban and San Diego data sets; see (Gillis et al., 2015). |
| Dataset Splits | No | The paper mentions generating synthetic data and using random initializations but does not specify explicit train/validation/test dataset splits with percentages or counts. |
| Hardware Specification | Yes | All tests are preformed using Matlab R2015a on a laptop Intel CORE i78550U CPU @1.8GHz 16GB RAM. |
| Software Dependencies | Yes | All tests are preformed using Matlab R2015a on a laptop Intel CORE i78550U CPU @1.8GHz 16GB RAM. |
| Experiment Setup | Yes | 3.3. Choice of Parameters for NMF Let us illustrate the choice of parameters for NMF. In the remainder of this paper, in the context of NMF, we will refer to IBPG as Algoritm 1 with the choice Tk = 2 (cyclic update of U and V ), and to IBPG-A with the choice Tk > 2 (U and V are updated several times). For IBPG and IBPG-A, we take L(k,m) 1 = L(k) 1 = ( V (k 1))T V (k 1) and L(k,m) 2 = L(k) 2 = ( U (k))T U (k) for m 1. We take β(k,m) i = 1/ L(k) i , γ(k,m) i = min τk 1 L(k 1) i L(k) i and α(k,m) i = α γ(k,m) i , where τ0 = 1, τk = 1 2(1 + q 1 + 4τ 2 k 1), γ = 0.99 and α = 1.01. Regarding IBP, we choose 1/β(k,m) i = 0.001 and α(k,m) i = α(k) = min( β, γ α(k 1)), with β = 1, γ = 1.01 and α(1) = 0.6. |