Minimization of Number of Comparisons of Worst-Case Linear Selection Algorithm

Abstract

In this paper, a problem statement of parametric optimization of Blum, Floyd, Pratt, Rivest, and Tarjan’s worst-case linear selection algorithm is considered. We define the parameter value that guarantees minimal theoretical upper bound of worst-case number of comparisons. We research dependence between number of comparisons and various values of parameter which is based on computational experiment.