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Michael T. Goodrich

Researcher at University of California, Irvine

Publications -  445
Citations -  14652

Michael T. Goodrich is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Planar graph & Time complexity. The author has an hindex of 61, co-authored 430 publications receiving 14045 citations. Previous affiliations of Michael T. Goodrich include New York University & Technion – Israel Institute of Technology.

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Fully Retroactive Approximate Range and Nearest Neighbor Searching

TL;DR: This work describes fully retroactive dynamic data structures for approximate range reporting and approximate nearest neighbor reporting and shows how to answer (1+e)-approximate nearest neighbor queries for any point in the past or present in O(logn) time.
Proceedings ArticleDOI

The Online House Numbering Problem: Min-Max Online List Labeling

TL;DR: This work introduces and study the online house numbering problem, where houses are added arbitrarily along a road and must be assigned labels to maintain their ordering along the road, and provides several algorithms that achieve interesting tradeoffs between upper bounds on the number of maximum relabels per element and theNumber of bits used by labels.
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Algorithms for Stable Matching and Clustering in a Grid

TL;DR: A discrete version of a geometric stable marriage problem originally proposed in a continuous setting, in which points in the plane are stably matched to cluster centers, so that each cluster center is apportioned a set of points of equal area, is studied.
Book ChapterDOI

Round-Trip Voronoi Diagrams and Doubling Density in Geographic Networks

TL;DR: In this article, the authors prove several new properties of two-site and two-color round-trip Voronoi diagrams in a geographic network, including a relationship between the doubling density of sites and an upper bound on the number of non-empty Voroni regions, which can be used in new algorithms asymptotically more efficient than previous known algorithms when the networks have reasonable distribution properties related to doubling density.
Proceedings ArticleDOI

Brief Announcement: Using Multi-Level Parallelism and 2-3 Cuckoo Filters for Set Intersection Queries and Sparse Boolean Matrix Multiplication

TL;DR: This work uses multi-level parallelism and a new type of data structures, known as 2-3 cuckoo filters, to answer set intersection queries faster than previous methods, with applications to improved sparse Boolean matrix multiplication.