Stochastic Frank-Wolfe for Composite Convex Minimization
Authors: Francesco Locatello, Alp Yurtsever, Olivier Fercoq, Volkan Cevher
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section presents the empirical performance of the proposed method for the stochastic kmeans clustering, covariance matrix estimation, and matrix completion problems. |
| Researcher Affiliation | Academia | Francesco Locatello? Alp Yurtsever Olivier Fercoq Volkan Cevher francesco.locatello@inf.ethz.ch {alp.yurtsever,volkan.cevher}@epfl.ch olivier.fercoq@telecom-paristech.fr ?Department of Computer Science, ETH Zurich, Switzerland LIONS, Ecole Polytechnique F ed erale de Lausanne, Switzerland LTCI, T el ecom Paris, Universit e Paris-Saclay, France |
| Pseudocode | Yes | Algorithm 1 SHCGM Input: x1 2 X, β0 > 0, d0 = 0 for k = 1, 2, . . . , do k = 9/(k + 8) βk = β0/(k + 8) 1 2 k = 4/(k + 7) 2 3 dk = (1 k)dk 1 + krxf(xk, !k) vk = dk +β 1 Axk proxβkg(Axk) sk = arg minx2X xk+1 = xk + k(sk xk) end for |
| Open Source Code | Yes | We include the code to reproduce the results in the supplements. |
| Open Datasets | Yes | with a sample of 1000 datapoints from the MNIST data2. 2Y. Le Cun and C. Cortes. Available at http://yann.lecun.com/exdb/mnist/ and We consider a test setup with the Movie Lens100k dataset3 [13]. 3F.M. Harper, J.A. Konstan. Available at https://grouplens.org/datasets/movielens/ and We consider a test setup with the Movie Lens1m dataset3 with 1 million ratings from 6000 users on 4000 movies. 3F.M. Harper, J.A. Konstan. Available at https://grouplens.org/datasets/movielens/ |
| Dataset Splits | Yes | We use the default ub.train and ub.test partitions provided with the original data. We partition the data into training and test samples with a 80/20 train/test split. |
| Hardware Specification | Yes | We performed the experiments in MATLAB R2018a using a computing system of 4 Intel Xeon CPU E5-2630 v3@2.40GHz and 16 GB RAM. |
| Software Dependencies | Yes | We performed the experiments in MATLAB R2018a using a computing system of 4 Intel Xeon CPU E5-2630 v3@2.40GHz and 16 GB RAM. |
| Experiment Setup | Yes | We use β0 = 1 for HCGM and β0 = 10 for SHCGM. We set these values by tuning both methods by trying β0 = 0.01, 0.1, ..., 1000. We set the model parameter for the nuclear norm constraint β1 = 7000, and the initial smoothing parameter β0 = 10. We set the model parameter β1 = 200000. We use β0 = 10 for SHCGM, and we set the step-size parameter γ = 1 for S3CCM. |