Constrained convex minimization via model-based excessive gap
Authors: Quoc Tran-Dinh, Volkan Cevher
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | 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 {quoc.trandinh, volkan.cevher}@epfl.ch |
| 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. |