{
  "id": "1712.04493",
  "version": "v1",
  "published": "2017-12-12T20:13:49.000Z",
  "updated": "2017-12-12T20:13:49.000Z",
  "title": "A Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks",
  "authors": [
    "Farshad Harirchi",
    "Omar A. Khalil",
    "Sijia Liu",
    "Paolo Elvati",
    "Angela Violi",
    "Alfred O. Hero"
  ],
  "comment": "The paper is presented at NIPS workshop on Advances in Modeling and Learning Interactions from Complex Data",
  "categories": [
    "math.OC",
    "cs.LG",
    "math.DS"
  ],
  "abstract": "In this paper, we propose an optimization-based sparse learning approach to identify the set of most influential reactions in a chemical reaction network. This reduced set of reactions is then employed to construct a reduced chemical reaction mechanism, which is relevant to chemical interaction network modeling. The problem of identifying influential reactions is first formulated as a mixed-integer quadratic program, and then a relaxation method is leveraged to reduce the computational complexity of our approach. Qualitative and quantitative validation of the sparse encoding approach demonstrates that the model captures important network structural properties with moderate computational load.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-12-12T20:13:49.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "chemical reaction network",
      "data-driven sparse-learning approach",
      "model reduction",
      "model captures important network structural",
      "captures important network structural properties"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}