Model Function Based Conditional Gradient Method with Armijo-like Line Search
Authors: Peter Ochs, Yura Malitsky
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our algorithm is shown to perform favorably on a sparse non-linear robust regression problem and we discuss the flexibility of the proposed framework in several matrix factorization formulations. [...] Figure 1 shows the data and the convergence of the objective value or the model improvement with respect to actual computation time. |
| Researcher Affiliation | Academia | 1University of G ottingen, G ottingen, Germany 2Saarland University, Saarbr ucken, Germany. Correspondence to: Peter Ochs <ochs@math.uni-sb.de>. |
| Pseudocode | Yes | Algorithm 1 (Model Based Conditional Gradient Method with Line Search). [...] Algorithm 2 (Armijo Line Search for Algorithm 1). |
| Open Source Code | No | The paper does not provide any links to open-source code or state that the code will be made publicly available. |
| Open Datasets | No | The data for the experiment is generated randomly with P = 100, M = 1000, µ = 80, a = 20, b = 5, and 80% of coefficients aj are randomly set to 0. |
| Dataset Splits | No | The paper mentions data generation but does not specify any training, validation, or test splits by percentages or counts. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions using |
| Experiment Setup | Yes | The data for the experiment is generated randomly with P = 100, M = 1000, µ = 80, a = 20, b = 5, and 80% of coefficients aj are randomly set to 0. [...] We solve the inner problem using the Primal Dual Hybrid Gradient Algorithm with preconditioning (Pock & Chambolle, 2011), which allows for step sizes that are automatically computed based on Ki. We use warm starting for all methods. Our algorithm FW-Comp Lin LS and Prox Linear LS solve the subproblem up to a certain accuracy and perform an Armijo-like line search in the direction of the approximate solution. |