The Knuth-Yao quadrangle-inequality speedup is a consequence of total-monotonicity

Authors:
Wolfgang W. Bein;Mordecai J. Golin;Lawrence L. Larmore;Yan Zhang
Affiliations:
University of Nevada, Las Vegas, NV;Hong Cong UST, Clear Water Bay, Kowloon, Hong Kong;University of Nevada, Las Vegas, NV;Hong Cong UST, Clear Water Bay, Kowloon, Hong Kong
Venue:
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Year:
2006

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Abstract

There exist several general techniques in the literature for speeding up naive implementations of dynamic programming. Two of the best known are the Knuth-Yao quadrangle inequality speedup and the SMAWK algorithm for finding the row-minima of totally monotone matrices. Although both of these techniques use a quadrangle inequality and seem similar they are actually quite different and have been used differently in the literature.In this paper we show that the Knuth-Yao technique is actually a direct consequence of total monotonicity. As well as providing new derivations of the Knuth-Yao result, this also permits showing how to solve the Knuth-Yao problem directly using the SMAWK algorithm. Another consequence of this approach is a method for solving online versions of problems with the Knuth-Yao property. The online algorithms given here are asymptotically as fast as the best previously known static ones. For example the Knuth-Yao technique speeds up the standard dynamic program for finding the optimal binary search tree of n elements from Θ(n3) down to O(n2), and the results in this paper allow construction of an optimal binary search tree in an online fashion (adding a node to the left or right of the current nodes at each step) in O(n) time per step.We conclude by discussing how the general technique described here is also applicable to later extensions of the Knuth-Yao result, such as those developed by Borchers and Gupta.