Budgeted Optimization with Constrained Experiments
Authors: Javad Azimi, Xiaoli Fern, Alan Fern
JAIR 2016 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our proposed methods for both settings using synthetic and real functions. The experimental results demonstrate the efficacy of the proposed methods. [...] 6. Experimental Results |
| Researcher Affiliation | Collaboration | Javad Azimi EMAIL Microsoft, Sunnyvale, CA, USA; Xiaoli Z. Fern EMAIL School of EECS, Oregon State University; Alan Fern EMAIL School of EECS, Oregon State University |
| Pseudocode | Yes | Algorithm 1 The Greedy Non-Sequential Algorithm [...] Algorithm 2 Accelerated Greedy Algorithm |
| Open Source Code | No | No explicit statement or link for the open-sourcing of the code described in this paper was found. |
| Open Datasets | Yes | The first three functions: Cosines, Rosenbrock, and Discontinuous are benchmarks that have been widely used in previous studies on stochastic optimization (Anderson, Moore, & Cohn, 2000; Brunato, Battiti, & Pasupuleti, 2006; Azimi et al., 2010). [...] For the former we utilize data collected as part of a study on biosolar hydrogen production (Burrows, Wong, Fern, Chaplen, & Ely, 2009) |
| Dataset Splits | No | The paper describes using test functions and a set of initial random points for policy evaluation, but does not specify conventional train/test/validation dataset splits for models or evaluation. |
| Hardware Specification | No | The paper mentions 'un-optimized matlab implementation' and describes run times, but does not provide specific hardware details (e.g., CPU/GPU models, memory, or machine specifications) used for running experiments. |
| Software Dependencies | No | The paper mentions using 'Gaussian process' and an 'un-optimized matlab implementation' but does not provide specific version numbers for any software, libraries, or solvers used to replicate the experiment. |
| Experiment Setup | Yes | In this paper we set κ = 0.02 and signal variance σf = y2max [...] We evaluate our proposed approaches considering three different slope values; slope = 0.1, 0.15, 0.30. [...] we divide each input dimension into 100 equal-length subintervals. [...] Each run starts with n = 5 randomly selected initial points [...] we fixed the total budget to B = 15 and examine the effect of the cost-model slope parameter over values 0.1, 0.15 and 0.3. In later experiments, we will consider larger budgets. |