Cost component analysis.
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.
Lörincz
András
A
Department of Information Systems, Eötvös Loránd University of Sciences, Pázmány Péter s. 1/c, Budapest, H-1117, Hungary. alorincz@axelero.hu
Póczos
Barnabás
B
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003365:Costs and Cost Analysis; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics