Engineering (Mathematical information Engineering)

Engineering (Mathematical information Engineering)

Mathematical Information Engineering Engineering: H29 4th year Summer semester

In the laboratory where we are assigned to, we will continue to read and share all the literature and papers on the field of expertise.

The textbook, known as a fine book of cryptography theory, is used to learn the fundamentals of computational algebra and the construction of public key cryptography and digital signatures, and the safety evaluation (cryptographic decoding).

  • Text: Johannes Buchmann, "Introduction to Cryptography," Springer, 2004.

(Prof. Tsuyoshi Takagi)

A textbook on data structures for high-speed processing of large amounts of data.

  • Text: G. Navarro, "Compact Data Structures: A Practical honest," Cambridge University Press, 2016.

(Prof. Kunihiko Sadakane)

I study the combinatorial optimization.0. Polytopes and Linear Programming, 1. Matroids and the Greedy Algorithm, 3. Matroid reads intersection.If you can afford 4. Proceed to matching.

  • Text: J. Lee, "A First course in Combinatorial Optimization", Cambridge University Press, 2004.

(Assoc. Prof. Hiroshi Hirai)

It is a unique survey paper written from the standpoint of numerical analysis on the adjoint method which appears in various fields such as numerical analysis and control theory.A variety of concepts such as numerical solution, variational, analysis dynamics, sensitivity analysis, and automatic differentiation are seen as one study.

  • Text: J. M. Sanz-serna, "symplectic Kutta schemes for adjoint Equations, Automatic differentiation, Optimal Control, and more," SIAM Review, 58 (2016), 3-33.

(Prof. Yu Matsuo)

This is a book that explains the analysis of numerical computation method by the function analytical method.The first is the basis of the function analysis.I will mainly read this first part and learn the basis of the function analysis, and take up some of the subjects related to the numerical calculation method after the midfield when it is possible to deal with time.

  • K. Atkinson, W. Han, "Theoretical numerical analysis-A Functional Analysis Framework", 3rd ed, Springer-verlag New York, 2009.

(Assoc. Prof. Kenichiro Tanaka)

We will learn about the introductory content of the major deviation principle which is important in probability theory.

  • Text: Shwartz, A. and Weiss, A. (1995). Large deviations for Performance Analysis: Queues, Communications and Computing, Chapman & Hall.

(Prof. Fumiyasu Komaki)

This is an introduction to statistical causal reasoning.

  • Text: J. Pearl, M. Glymour, and N.P. Jewell, "Causal inference in Statistics: A Primer," Wiley, 2016.

(Associate Professor Tomoya Kiyoshi)

Learn about the principles and applications of deep learning.

  • Text: Ian Goodfellow, Yoshua Bengio and Aaron Courville, "Deep Learning," MIT Press, 2015

(Prof. Kenji Shanxi)

The convergence of stochastic variables in statistics and machine learning is a fundamental concept.Learn more about the convergence of stochastic variables and their applications (central limit theorem, convergence of estimated amounts) through the turn of this document.

  • Text: W. Van der Vaart, "Asymptotic Statistics", Cambridge University Press, 1998.

(Assoc. Prof. Great Suzuki)

We introduce algebraic and geometric methods for discrete optimization (integer programming).In particular, reading the first and second chapters, we will learn how to use linear programming, convex optimization, and number geometry in the theory of Integer programming.

  • Text: J. A. De Loera, R. Hemmecke, M. Koeppe: "Algebraic and Geometric Ideas in the Theory of Discrete Optimization," SIAM, 2013.

(Prof. Satoru Rock)

Graph theory is one of the standard textbooks.We will study the typical theorem on the degree of coupling, planar graphs, and coloring, and learn about extreme problems if possible in time.

  • Text: R. Diestel, "Graph Theory (Fourth Edition)", Springer, 2010.

(Associate Professor Shinichi Tanigawa)

We study algorithms on the Elementary, integer and polynomial rings that are necessary to understand cryptography theory.In particular, we study the algorithm to solve the prime judgment algorithm, factoring algorithm, and discrete logarithm problem.

  • Text: V. Shoup, A computational Introduction to Number Theory and Algebra (Second Edition), Cambridge University Press, New York, 2009.

(Kunihiro Noboru, associate professor)