Computing equilibria on large multicommodity networks: An application of truncated quadratic programming algorithms

We present a general scheme for improving the asymptotic behavior of a given nonlinear programming algorithm without incurring a significant increase in storage overhead. To enhance the rate of convergence we compute search directions by partially solving a sequence of quadratic programming (QP) problems as suggested by Dembo [6]. The idea is illustrated on a class of extremely large nonlinear programming problems arising from traffic equilibrium calculations using both the Frank-Wolfe and PARTAN algorithms to partially solve the QP subproblems. Computational results indicate that the convergence rate of the underlying algorithm is indeed enhanced significantly when Frank-Wolfe is used to solve the QP subproblems but only marginally so in the case of PARTAN. It is conjectured, and supported by the theory [11], that with better algorithms for the QP subproblems the improvements due to the proposed framework would be more marked.

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