Sub-exponential for Subset Sum on the total bit length of the input Was crawling the internet, and found this in a paper (1)

"[...], a variant of dynamic programming called
dynamic dynamic programming has been shown to have a worst-case sub-exponential time complexity of $2^{O(\sqrt x)}$ when the total bit length $x$ of the input set is used as the complexity parameter."

As I understand this claim violates the Exponential Time Hypothesis, and there has been some answers in the forum clearly stating no such sub-exponential algorithm exists, would be interesting to know what the experts in the area think about the proposed algorithm in terms of worst case complexity.

Direct link to the paper (2) explaining the dynamic dynamic approach.

(1) Thomas E. O’Neil -An Empirical Study of Algorithms for the Subset Sum Problem 

(2) Thomas E. O’Neil, Scott Kerlin - A Simple $2^{O(\sqrt x)}$ Algorithm for PARTITION and SUBSET SUM
This doesn't violate the exponential time hypothesis.  The exponential time hypothesis says that k-SAT takes exponential time, i.e., $\Omega(2^{cn})$ for some constant $c&gt;0$.  So if you found a $O(2^{\sqrt{n}})$ time algorithm for k-SAT, that would violate the exponential time hypothesis -- but a a $O(2^{\sqrt{n}})$ time algorithm for subset sum does not violate the hypothesis.