Stochastic subgradient for composite convex optimization with functional constraints
Authors: Ion Necoara, Nitesh Kumar Singh
JMLR 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 5 detailed numerical simulations are provided that support the effectiveness of our method in real problems. ... Tables 1 and 2 give the results of our algorithm SSP for robust (ρ > 0) and nominal (ρ = 0) classification formulations. We choose the parameters λ = 0.1, 0.2, 0.3, β = 1.96, and stopping criterion 10 2. We consider a dataset of CT scan images... We also resize all images into 190 × 190 pixels. |
| Researcher Affiliation | Academia | Ion Necoara EMAIL Automatic Control and Systems Engineering Department, University Politehnica Bucharest, Spl. Independentei 313, 060042 Bucharest, Romania. Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy, 050711 Bucharest, Romania. Nitesh Kumar Singh EMAIL Automatic Control and Systems Engineering Department, University Politehnica Bucharest, Spl. Independentei 313, 060042 Bucharest, Romania. |
| Pseudocode | Yes | Algorithm 1 (SSP): Choose x0 ∈ Y and stepsizes αk > 0 and β ∈ (0, 2) For k ≥ 0 repeat: Sample independently ζk ∼ P1 and ξk ∼ P2 and update : vk = proxαkg(·,ζk) (xk − αk ∇f(xk, ζk)) (5) zk = vk − β (h(vk, ξk))+ ∇h(vk, ξk) 2 ∇h(vk, ξk) (6) xk+1 = ΠY(zk). (7) |
| Open Source Code | No | The codes were written in Matlab and run on a PC with i7 CPU at 2.1 GHz and 16 GB memory. (No specific link or statement about open-source release is provided for the authors' own code.) |
| Open Datasets | Yes | We consider a dataset of CT scan images having two classes, covid and non-covid, available at https://www.kaggle.com/plameneduardo/ sarscov2-ctscan-dataset. ... PMU-UD, sobar-72 and divorce datasets available on https://archive-beta.ics.uci. edu/ml/datasets; and the rest from LIBSVM library https://www.csie.ntu.edu.tw/ ~cjlin/libsvmtools/datasets/. |
| Dataset Splits | Yes | To implement our algorithm we have taken 1488 data in which 751 are of Covid patients and 737 of Non-Covid patients. Then, we divide them into training data and testing data. For training data we have taken 1240 images in which 626 are of Covid and 614 are of Non-Covid. For testing data we have taken 248 images in which 125 are of Covid and 123 of Non-Covid. ... We divided each dataset into 80% for training and 20% for testing. |
| Hardware Specification | Yes | The codes were written in Matlab and run on a PC with i7 CPU at 2.1 GHz and 16 GB memory. |
| Software Dependencies | No | The codes were written in Matlab and run on a PC with i7 CPU at 2.1 GHz and 16 GB memory. (The version of Matlab is not specified, nor are specific versions for other software components like the 'Matlab solver lsqlin' or 'LIBSVM library' mentioned later.) |
| Experiment Setup | Yes | We choose the parameters λ = 0.1, 0.2, 0.3, β = 1.96, and stopping criterion 10 2. ... Then, the robust sparse classification problem can be formulated as: min w,d,u λ i=1 ui + w 1 ... We choose the parameters λ = 0.1, 0.2, 0.3, β = 1.96, and stopping criterion 10 2. ... For the constrained least-squares problem, we consider an adaptive stepsize: αk = δ AT ζkxk − bζk 2 / Aζk(AT ζkxk − bζk) 2 , where δ ∈ (0, 2). ... Both algorithms were stopped when max(Ax − b , (Cx − d)+ ) ≤ 10 −3. |