We present a general framework of designing efficient dynamic approximate algorithms for
optimization problems on undirected graphs. In particular, we develop a technique that …

Dynamic algorithms operate on inputs undergoing updates, eg, insertions or deletions of
edges or vertices. After processing each update, the algorithm has to answer queries …

The Fréchet distance is a popular similarity measure between curves. For some
applications, it is desirable to match the curves under translation before computing the …

The discrete Fréchet distance is a popular measure for comparing polygonal curves. An
important variant is the discrete Fréchet distance under translation, which enables detection …

Computing the similarity of two point sets is a ubiquitous task in medical imaging, geometric
shape comparison, trajectory analysis, and many more settings. Arguably the most basic …

Detecting commuting patterns or migration patterns in movement data is an important
problem in computational movement analysis. Given a trajectory, or set of trajectories, this …

In this article, we study a wide range of variants for computing the (discrete and continuous)
Fréchet distance between uncertain curves. An uncertain curve is a sequence of uncertainty …

Graphs naturally appear in several real-world contexts including social networks, the web
network, and telecommunication networks. While the analysis and the understanding of …

Fine-grained complexity theory is the area of theoretical computer science that proves
conditional lower bounds based on the Strong Exponential Time Hypothesis and similar …

Abstract $\newcommand {\frechet}{Fréchet} $ The (discrete)\frechet\distance (DFD) is a
popular similarity measure for curves. Often the input curves are not aligned, so one of them …