2017 Mathematical Optimization: Theory and Algorithms

Font size  SML

Register update notification mail Add to favorite lecture list
Academic unit or major
Graduate major in Mathematical and Computing Science
Instructor(s)
Fukuda Mituhiro  Yamashita Makoto 
Class Format
Lecture     
Media-enhanced courses
Day/Period(Room No.)
Tue5-6(W832)  Fri5-6(W832)  
Group
-
Course number
MCS.T402
Credits
2
Academic year
2017
Offered quarter
3Q
Syllabus updated
2017/4/12
Lecture notes updated
2017/11/21
Language used
English
Access Index

Lecture

Lecture 1 Convex sets

2017.9.26(Tue.) 5-6Session

Lecture

Lecture 2 Lipschitz continuous differentiable functions

2017.9.29(Fri.) 5-6Session

Lecture

Lecture 3 Optimality conditions for differentiable functions

2017.10.3(Tue.) 5-6Session

Lecture

Lecture 4 Minimization algorithms for unconstrained optimization problems

2017.10.10(Tue.) 5-6Session

Lecture

Lecture 5  

2017.10.13(Fri.) 5-6Session

Lecture

Lecture 6 Steepest descent method and the Newton method

2017.10.17(Tue.) 5-6Session

Lecture

Lecture 7 Conjugate gradient methods, quasi-Newton methods

2017.10.20(Fri.) 5-6Session

Lecture

Lecture 8 Differentiable convex function

2017.10.24(Tue.) 5-6Session

Lecture

Lecture 9 General assignment to check the comprehension

2017.10.27(Fri.) 5-6Session

Lecture

Lecture 10 Differentiable convex functions with Lipschitz continuous gradients

2017.10.31(Tue.) 5-6Session

Lecture

Lecture 11 Worse case analysis for gradient based methods

2017.11.3(Fri.) 5-6Session

Lecture

Lecture 12 Steepest descent method for differentiable convex functions

2017.11.7(Tue.) 5-6Session

Lecture

Lecture 13 Estimate sequence in accelerated gradient methods for differentiable convex functions

2017.11.10(Fri.) 5-6Session

Lecture

Lecture 14 Accelerated gradient method for differentiable convex functions

2017.11.14(Tue.) 5-6Session

Lecture

Lecture 15 Optimality of accelerated gradient method for differentiable convex functions

2017.11.17(Fri.) 5-6Session

Lecture

Lecture 16 Accelerated gradient methods for min-max problems

2017.11.21(Tue.) 5-6Session

Get Adobe Reader

It is necessary for those who refer to the PDF file to use "Adobe Reader" as the plug-in software of Adobe System Company.
If you don't have the software, please download from this item (free).

Creative Commons License