This book constitutes the thoroughly refereed post-conference proceedings of the Kyoto Conference on Computational Geometry and Graph Theory, KyotoCGGT 2007, held in Kyoto, Japan, in June 2007, in honor of Jin Akiyama and Vasek Chvatal, on the occasion of their 60th birthdays.

The 19 revised full papers, presented together with 5 invited papers, were carefully selected during two rounds of reviewing and improvement from more than 60 talks at the conference. All aspects of Computational Geometry and Graph Theory are covered, including tilings, polygons, impossible objects, coloring of graphs, Hamilton cycles, and factors of graphs.

This volume contains the proceedings of the 14th Annual International S- posium on Algorithms and Computation (ISAAC 2003), held in Kyoto, Japan, 15 17 December 2003. In the past, it was held in Tokyo (1990), Taipei (1991), Nagoya (1992), Hong Kong (1993), Beijing (1994), Cairns (1995), Osaka (1996), Singapore (1997), Taejon (1998), Chennai (1999), Taipei (2000), Christchurch (2001), and Vancouver (2002). ISAACisanannualinternationalsymposiumthatcoverstheverywiderange of topics in algorithms and computation. The main purpose of the symposium is to provide a forum for researchers working in algorithms and the theory of computation where they can exchange ideas in this active research community. In response to our call for papers, we received unexpectedly many subm- sions, 207 papers. The task of selecting the papers in this volume was done by our program committee and referees. After a thorough review process, the committee selected 73 papers. The selection was done on the basis of originality and relevance to the ?eld of algorithms and computation. We hope all accepted papers will eventally appear in scienti?c journals in more polished forms. The best paper award was given for On the Geometric Dilation of Finite Point Sets to Annette Ebbers-Baumann, Ansgar Grune ] and Rolf Klein. Two eminent invited speakers, Prof. Andrew Chi-Chih Yao of Princeton University and Prof. Takao Nishizeki of Tohoku University, contributed to this proceedings."

This book presents the refereed proceedings of the 6th International Symposium on Algorithms and Computation, ISAAC '95, held in Cairns, Australia, in December 1995.
The 45 revised full papers presented together with the abstracts of three invited talks were selected from a total of 130 submissions. The papers address many current aspects of research and advanced applications of algorithms and computations; among the topics covered are graph theory and graph algorithms, computational geometry, computational logics, searching and sorting, approximation and optimization, algebraic manipulation, and coding.

This open access book gives an overview of cutting-edge work on a new paradigm called the "sublinear computation paradigm," which was proposed in the large multiyear academic research project "Foundations of Innovative Algorithms for Big Data." That project ran from October 2014 to March 2020, in Japan. To handle the unprecedented explosion of big data sets in research, industry, and other areas of society, there is an urgent need to develop novel methods and approaches for big data analysis. To meet this need, innovative changes in algorithm theory for big data are being pursued. For example, polynomial-time algorithms have thus far been regarded as "fast," but if a quadratic-time algorithm is applied to a petabyte-scale or larger big data set, problems are encountered in terms of computational resources or running time. To deal with this critical computational and algorithmic bottleneck, linear, sublinear, and constant time algorithms are required.The sublinear computation paradigm is proposed here in order to support innovation in the big data era. A foundation of innovative algorithms has been created by developing computational procedures, data structures, and modelling techniques for big data. The project is organized into three teams that focus on sublinear algorithms, sublinear data structures, and sublinear modelling. The work has provided high-level academic research results of strong computational and algorithmic interest, which are presented in this book. The book consists of five parts: Part I, which consists of a single chapter on the concept of the sublinear computation paradigm; Parts II, III, and IV review results on sublinear algorithms, sublinear data structures, and sublinear modelling, respectively; Part V presents application results. The information presented here will inspire the researchers who work in the field of modern algorithms.

This open access book gives an overview of cutting-edge work on a new paradigm called the "sublinear computation paradigm," which was proposed in the large multiyear academic research project "Foundations of Innovative Algorithms for Big Data." That project ran from October 2014 to March 2020, in Japan. To handle the unprecedented explosion of big data sets in research, industry, and other areas of society, there is an urgent need to develop novel methods and approaches for big data analysis. To meet this need, innovative changes in algorithm theory for big data are being pursued. For example, polynomial-time algorithms have thus far been regarded as "fast," but if a quadratic-time algorithm is applied to a petabyte-scale or larger big data set, problems are encountered in terms of computational resources or running time. To deal with this critical computational and algorithmic bottleneck, linear, sublinear, and constant time algorithms are required.The sublinear computation paradigm is proposed here in order to support innovation in the big data era. A foundation of innovative algorithms has been created by developing computational procedures, data structures, and modelling techniques for big data. The project is organized into three teams that focus on sublinear algorithms, sublinear data structures, and sublinear modelling. The work has provided high-level academic research results of strong computational and algorithmic interest, which are presented in this book. The book consists of five parts: Part I, which consists of a single chapter on the concept of the sublinear computation paradigm; Parts II, III, and IV review results on sublinear algorithms, sublinear data structures, and sublinear modelling, respectively; Part V presents application results. The information presented here will inspire the researchers who work in the field of modern algorithms.