ON PARTIAL UPDATING IN A POTENTIAL REDUCTION LINEAR-PROGRAMMING ALGORITHM OF KOJIMA, MIZUNO, AND YOSHISE

We consider partial updating in Kojima, Mizuno, and Yoshise's primal-dual potential reduction algorithm for linear programming. We use a simple safeguard condition to control the number of updates incurred on combined primal-dual steps. Our analysis allows for unequal steplengths in the primal and dual variables, which appears to be a computationally significant factor for primal-dual methods. The safeguard we use is a primal-dual Goldstein-Armijo condition, modified to deal with the unequal primal and dual steplengths.