Given a directed weighted graph
G=(V,E) undergoing vertex insertions \emph{and} deletions, the All-Pairs Shortest Paths (APSP) problem asks to maintain a data structure that processes updates efficiently and returns after each update the distance matrix to the current version of
G. In two breakthrough results, Italiano and Demetrescu [STOC '03] presented an algorithm that requires
... [Show full abstract] \emph{amortized} update time, and Thorup showed in [STOC '05] that \emph{worst-case} update time can be achieved. In this article, we make substantial progress on the problem. We present the following new results: (1) We present the first deterministic data structure that breaks the worst-case update time bound by Thorup which has been standing for almost 15 years. We improve the worst-case update time to and to for unweighted graphs. (2) We present a simple deterministic algorithm with worst-case update time ( for unweighted graphs), and a simple Las-Vegas algorithm with worst-case update time ( for unweighted graphs) that works against a non-oblivious adversary. Both data structures require space . These are the first exact dynamic algorithms with truly-subcubic update time \emph{and} space usage. This makes significant progress on an open question posed in multiple articles [COCOON'01, STOC '03, ICALP'04, Encyclopedia of Algorithms '08] and is critical to algorithms in practice [TALG '06] where large space usage is prohibitive. Moreover, they match the worst-case update time of the best previous algorithms and the second algorithm improves upon a Monte-Carlo algorithm in a weaker adversary model with the same running time [SODA '17].