The Knuth-Yao Quadrangle-inequality Speedup is a Consequence of Total-monotonicity

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Conference Proceeding

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ACM Transactions on Algorithms (TALG)





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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 article 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 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 article allow construction of an optimal binary search tree in an online fashion (adding a node to the left or the right of the current nodes at each step) in O(n) time per step.



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