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..
Parameterizing Branch-and-Bound Search Trees to Learn Branching Policies
Authors: Giulia Zarpellon, Jason Jo, Andrea Lodi, Yoshua Bengio3931-3939
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on MILP benchmark instances clearly show the advantages of incorporating an explicit parameterization of the state of the B&B search tree to modulate the branching decisions, in terms of both higher accuracy and smaller B&B trees. |
| Researcher Affiliation | Academia | 1Polytechnique Montr eal 2Mila 3Universit e de Montr eal |
| Pseudocode | No | The paper describes the algorithms and models used (e.g., B&B, DNN architectures), but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code, data and supplementary materials can be found at: https://github.com/ds4dm/branch-search-trees |
| Open Datasets | Yes | Thus we select 27 heterogeneous problems from realworld MILP benchmark libraries (Bixby et al. 1998; Koch et al. 2011; Gleixner et al. 2019; Mittelmann 2020), focusing on instances whose tree exploration is on average relatively contained (in the tens/hundreds of thousands nodes, max.) and whose optimal value is known. |
| Dataset Splits | Yes | The final composition of train, validation and test sets is summarized in Table 1(b). |
| Hardware Specification | No | Further details on the solver parameters and hardware settings are reported in the SM. The main text does not specify exact hardware components like CPU/GPU models or memory. |
| Software Dependencies | Yes | We use SCIP 6.0.1. |
| Experiment Setup | Yes | Our hyper-parameter search spans: learning rate LR {0.01, 0.001, 0.0001}, hidden size h {32, 64, 128, 256}, and depth d {2, 3, 5}. The factor by which units of No Tree are reduced is 2, and we fix INF = 8. We use Py Torch (Paszke et al. 2019) to train the models for 40 epochs, reducing LR by a factor of 10 at epochs 20 and 30. We train both IL policies using ADAM (Kingma and Ba 2015) with default β1 = 0.9, β2 = 0.999, and weight decay 1 10 5. |