M
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.
Papers
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Planar Drawings of Higher-Genus Graphs
TL;DR: In this article, the authors give polynomial-time algorithms that can take a graph G with a given combinatorial embedding on an orientable surface S of genus g and produce a planar drawing of G in R^2, with a bounding face defined by a polygonal schema P for S. Their drawings are planar, but they allow for multiple copies of vertices and edges on P's boundary.
Posted Content
Scheduling Autonomous Vehicle Platoons Through an Unregulated Intersection
TL;DR: It is shown that the more general problem of scheduling autonomous platoons through an intersection that includes both a-way merge, for non-constant $k$, and a crossing of two-way traffic is NP-complete.
Proceedings ArticleDOI
Blocking for external graph searching
TL;DR: This paper considers the problem of using disk blocks efficiently in searching graphs that are too large to fit in internal memory and gives matching upper and lower bounds for complete d-ary trees and d-dimensional grid graphs, as well as for classes of general graphs that intuitively speaking have a close to uniform number of neighbors around each vertex.
Book
Data Structures and Algorithms in Python
TL;DR: Data Structures and Algorithms in Python is the first authoritative object-oriented book available for Python data structures, designed to provide a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation.
Book ChapterDOI
Verifiable Zero-Knowledge Order Queries and Updates for Fully Dynamic Lists and Trees
TL;DR: This work proposes a three-party model for maintaining a dynamic data structure that supports verifiable and privacy-preserving zero-knowledge queries and gives efficient constructions supporting this model for order queries on data organized in lists, trees, and partially-ordered sets of bounded dimension.