A Universal Primal-Dual Convex Optimization Framework
Authors: Alp Yurtsever, Quoc Tran Dinh, Volkan Cevher
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section illustrates the scalability and the flexibility of our primal-dual framework using some applications in the quantum tomography (QT) and the matrix completion (MC). Figure 1: The convergence behavior of algorithms for the q 14 qubits QT problem. Figure 2: The performance of the algorithms for the MC problems. |
| Researcher Affiliation | Academia | Alp Yurtsever: Quoc Tran-Dinh; Volkan Cevher: Laboratory for Information and Inference Systems, EPFL, Switzerland {alp.yurtsever, volkan.cevher}@epfl.ch ; Department of Statistics and Operations Research, UNC, USA quoctd@email.unc.edu |
| Pseudocode | Yes | Algorithm 1 (Universal Primal-Dual Gradient Method p Uni PDGradq) and Algorithm 2 (Accelerated Universal Primal-Dual Gradient Method p Acc Uni PDGradq) |
| Open Source Code | No | The paper mentions using existing functions like MATLAB's eigs and PROPACK's lansvd but does not state that the authors' own implementation code for their framework is open-source or available. |
| Open Datasets | Yes | We apply our algorithms to (20) and (21) using the Movie Lens 100K dataset. |
| Dataset Splits | No | The paper mentions "test and training data partition" for MovieLens but does not specify exact percentages or sample counts for these splits, nor does it mention a validation split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions "MATLAB’s eigs function" and "lansvd function (MATLAB version) from PROPACK [21]" but does not specify version numbers for MATLAB or the functions. |
| Experiment Setup | Yes | We set ϵ 2 ˆ 10 4 for our methods and have a wall-time 2ˆ104s in order to stop the algorithms. we set the target accuracy ϵ 10 3, and we choose the tuning parameter κ 9975{2 as in [20]. We use lansvd function (MATLAB version) from PROPACK [21] to compute the top singular vectors, and a simple implementation of the power method to find the top singular value in the line-search, both with 10 5 relative error tolerance. |