This papers proposes techniques for writing self-adjusting programs that can adjust to any change to their data (e.g., inputs, decisions made during the computation) etc. We show that the techniques are efficient by considering a number of applications including several sorting algorithms, and the Graham Scan, and the quick hull algorithm for computing convex hulls. We show that the techniques ...

Self-adjusting computation is a paradigm for programming incremental computations that efficiently respond to input changes by updating the output in time proportional to the changes in the structure of the computation. This dissertation defends the thesis that high-level programming abstractions improve the experience of reading, writing, and reasoning about and the efficiency of self-adjustin...

In this paper we explore two themes in data structure design: amortized computational complexity and self-adjustment. We are motivated by the following observations. In most applications of data structures, we wish to perform not just a single operation but a sequence of operations, possibly having correlated behavior. By averaging the running time per operation over a worst-case sequence of op...

The reactive diffracting trees are known efficient distributed data structures for supporting synchronization. They not only distribute a set of processes to smaller groups accessing different parts of the memory in a global coordinated manner, but also adjust their size in order to attain efficient performance across different levels of contention. However, the existing reactive adjustment pol...