Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Constrained convex minimization via model-based excessive gap
Authors: Quoc Tran-Dinh, Volkan Cevher
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Numerical illustrations 5.1. Theoretical vs. practical bounds. We demonstrate the empirical performance of Algorithm 1 w.r.t. its theoretical bounds via a basic non-overlapping sparse-group basis pursuit problem: ... The empirical performance of two variants: (2P1D) and (1P2D) of Algorithm 1 is shown in Figure 1. ... 5.2. Binary linear support vector machine. This example is concerned with the following binary linear support vector machine problem: ... Now, we apply the (1P2D) variant to solve (24). We test this algorithm on (24) and compare it with Lib SVM [32] using two problems from the Lib SVM data set... |
| Researcher Affiliation | Academia | Quoc Tran-Dinh and Volkan Cevher Laboratory for Information and Inference Systems (LIONS) Ecole Polytechnique F ed erale de Lausanne (EPFL), CH1015-Lausanne, Switzerland EMAIL |
| Pseudocode | Yes | Algorithm 1: (A primal-dual algorithmic template using model-based excessive gap) |
| Open Source Code | No | The paper does not contain any statement about releasing the source code for its proposed methods, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We test this algorithm on (24) and compare it with Lib SVM [32] using two problems from the Lib SVM data set available at http://www.csie. ntu.edu.tw/ cjlin/libsvmtools/datasets/. |
| Dataset Splits | No | We randomly select 30% data in a1a and news20 to form a test set, and the remaining 70% data is used for training. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU/GPU models, memory, or specific computing environments used for running the experiments. |
| Software Dependencies | No | The paper mentions external tools like 'SDPT3 interior-point solver [30]', 'FISTA [31]', and 'Lib SVM [32]', but it does not specify version numbers for these or any other software dependencies needed for replication. |
| Experiment Setup | Yes | In this test, we fix xc = 0n and db(x, xc) := (1/2) x 2. ... In the (2P1D) scheme, we set γ0 = β0 = p Lg, while, in the (1P2D) scheme, we set γ0 := 2 / (2 * A * (K + 1)) with K := 10^4 and generate the theoretical bounds defined in Theorem 1. ... With a kick-factor of ck = 0.02/τk and adaptive xk c, both turned variants (2P1D) and (1P2D) significantly outperform theoretical predictions. |