Cost component analysis

András Lorincz, Barnabaas Póczos

Research output: Contribution to journalArticle


In optimizations the dimension of the problem may severely, sometimes exponentially increase optimization time. Parametric function approximatiors (FAPPs) have been suggested to overcome this problem. Here, a novel FAPP, cost component analysis (CCA) is described. In CCA, the search space is resampled according to the Boltzmann distribution generated by the energy landscape. That is, CCA converts the optimization problem to density estimation. Structure of the induced density is searched by independent component analysis (ICA). The advantage of CCA is that each independent ICA component can be optimized separately. In turn, (i) CCA intends to partition the original problem into subproblems and (ii) separating (partitioning) the original optimization problem into subproblems may serve interpretation. Most importantly, (iii) CCA may give rise to high gains in optimization time. Numerical simulations illustrate the working of the algorithm.

Original languageEnglish
Pages (from-to)183-192
Number of pages10
JournalInternational journal of neural systems
Issue number3
Publication statusPublished - Jun 2003


  • Combinational explosion
  • Independent component analysis
  • Optimization
  • Search

ASJC Scopus subject areas

  • Computer Networks and Communications

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