Fundamentals of Mathematical and Computing Sciences:Applied Mathematical Science

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Lecturer: Miyoshi Naoto Fukuda Mituhiro Suzuki Taiji

Place: Fri5-6(W832)

Credits: Lecture2 Exercise0 Experiment0

Code: 75052

Syllabus updated: 2015/3/16

Lecture notes updated: 2015/7/27

Access Index

Semester: Spring Semester

Lecture

Lecture 1 Conic linear program

2015.4.10(Fri.) 5-6session

fmcs-ams-15-1-1(62KB)

Lecture

Lecture 2 Duality theorem

2015.4.17(Fri.) 5-6session

fmcs-ams-15-1-2(52KB)

Lecture

Lecture 3 Linear program relaxation

2015.4.24(Fri.) 5-6session

fmcs-ams-15-1-3(78KB)

Lecture

Lecture 4 Semidefinite program relaxation

2015.5.1(Fri.) 5-6session

fmcs-ams-15-1-4(113KB)

Lecture

Lecture 5 POP and second-order cone program relaxation

2015.5.8(Fri.) 5-6session

fmcs-ams-15-1-5(63KB)

fmcs-ams-report-15-1(44KB)

Lecture

Lecture 6 Probability space revisited

2015.5.22(Fri.) 5-6session

fmcs-ams-15-2-1(50KB)

Lecture

Lecture 7 縲

2015.5.29(Fri.) 5-6session

Lecture

Lecture 8 Measure and probability

2015.6.5(Fri.) 5-6session

fmcs-ams-15-2-2(37KB)

Lecture

Lecture 9 Integration and Expectation

2015.6.12(Fri.) 5-6session

fmcs-ams-15-2-3(41KB)

Lecture

Lecture 10 Convergence theorems in integrations

2015.6.19(Fri.) 5-6session

fmcs-ams-15-2-4(46KB)

Lecture

Lecture 11 Low rank matrix estimation: Problem formulation

2015.6.26(Fri.) 5-6session

Problem Setting: From vector to matrix(66KB)

Lecture

Lecture 12 Low rank matrix estimation: Estimation method

2015.7.3(Fri.) 5-6session

Estimation methods(60KB)

Lecture

Lecture 13 縲

2015.7.10(Fri.) 5-6session

Computational methods(62KB)

Lecture

Lecture 14 縲

2015.7.17(Fri.) 5-6session

Statistical property(51KB)

Lecture

Lecture 15 Low rank matrix estimation: Statistical property

2015.7.24(Fri.) 5-6session

Reporting assignment(31KB)

SVD of large matrix(35KB)

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Fundamentals of Mathematical and Computing Sciences:Applied Mathematical Science

Lecture 1 Conic linear program

Lecture 2 Duality theorem

Lecture 3 Linear program relaxation

Lecture 4 Semidefinite program relaxation

Lecture 5 POP and second-order cone program relaxation

Lecture 6 Probability space revisited

Lecture 7 縲

Lecture 8 Measure and probability

Lecture 9 Integration and Expectation

Lecture 10 Convergence theorems in integrations

Lecture 11 Low rank matrix estimation: Problem formulation

Lecture 12 Low rank matrix estimation: Estimation method

Lecture 13 縲

Lecture 14 縲

Lecture 15 Low rank matrix estimation: Statistical property

Lecture 7 縲

Lecture 13 縲

Lecture 14 縲