Computational Complexity Results for Genetic Programming and the Sorting Problem

Genetic Programming (GP) has found various applications. Understanding this type of algorithm from a theoretical point of view is a challenging task. The first results on the computational complexity of GP have been obtained for problems with isolated program semantics. With this paper, we push forward the computational complexity analysis of GP on a problem with dependent program semantics. We study the well-known sorting problem in this context and analyze rigorously how GP can deal with different measures of sortedness.Comment: 12 page

On the SCALE Algorithm for Multiuser Multicarrier Power Spectrum Management

This paper studies the successive convex approximation for low complexity (SCALE) algorithm, which was proposed to address the weighted sum rate (WSR) maximized dynamic power spectrum management (DSM) problem for multiuser multicarrier systems. To this end, we first revisit the algorithm, and then present geometric interpretation and properties of the algorithm. A geometric programming (GP) implementation approach is proposed and compared with the low-complexity approach proposed previously. In particular, an analytical method is proposed to set up the default lower-bound constraints added by a GP solver. Finally, numerical experiments are used to illustrate the analysis and compare the two implementation approaches.Comment: 8 pages, 5 figures; IEEE Transactions on Signal Processing, vol. 60, no. 9, Sep. 201