Experimental research of primal and dual interior point algorithms on class of nonlinear network problems
Primal and dual interior point algorithms for solving a class of mutually-dual problems of convex optimization is investigated in this paper. Algorithms are applied in original and dual problem. Dual problem is obtained using Legendre-Fenchel conjugate functions. Description of primal interior point algorithm using two kinds of penalty functions is given. Effective ways of algorithm realization is revealed based on results of numerical experiments. A property of algorithms is obtained in experiments: dual variables converges by iterations to their optimal values faster than primal variables.