Optimization and computational approaches (Fall 2021)

Optimization and Computational approaches for Ph.D. and MS students (Fall 2021)

This course is devoted to advanced and more recent topics in Optimization and computational approaches. Indeed I selected some topics in computational methods and Optimization problems.

Link for class https://vc15.sbu.ac.ir/class-4001161821101/

Link for my previous lectures on Computational Physics

Link for my lectures on Optimization (Khajeh Nasir Digital Library, SBU VPN needed)

Some topics to teach are as follows:

Probability Distribution functions and transformations
Correlation functions, Two-point correlation function
Monte Carlo simulation
Data modeling
Genetic Algorithms

Course subjects and program (Download)
A good movie presented by Pooyan Goodarzi to connect the server, remotely (Link)
A good link for shell script programming (Link)
A good link for programming tutorials (Link)
Hartmann, Alexander K., and Heiko Rieger. Optimization algorithms in physics. Vol. 2. Berlin: Wiley-Vch, 2002.
Hartmann, Alexander K., and Heiko Rieger, eds. "New optimization algorithms in physics." (2004): 134411.
Mezard, Marc, and Andrea Montanari. Information, physics, and computation. Oxford University Press, 2009.
Computational Physics By RUBIN H. LANDAU, MANUEL JOSE PAEZ and CRISTIAN C. BORDEIANU (See this link)
A good presentation by Kip R. Irvine for number representation (Download)
Some bash samples (Download)
A good text for commands in Fortran, C++, Matlab (Download)
VPython
Some necessary things for programming skills (Download)
A good paper for data analysis in cosmology by Licia Verde, arXiv:0712.3028
Online numerical recipes (http://www.numerical.recipes)
My lectures on Errors and PDF (Download) (Download)
Some of my Python programs (Download)
Visualization by Matlab (link)
Discretization approaches (Download) (Download)
My note about deterministic Fractals (Download) & (see this link)
A good reference for errors analysis (see this link)
A proper series for Machine learning (Part 1), (part 2)
Some good Books for Machine learning and related topics, http://www.aghamousa.com/data-science-books/
School and Workshop on statistical analysis of stochastic fields (Link) (Link)

My lectures

Some of my previous lectures on computational Physics (Click here)

Preliminary part (Download) Class's Video (link)
Number representation (14000706) Class's Video (link)
Data Science (Download)
Error estimation (error estimation) my note included (error estimation)
Optimization: General view (Download) Class's Video (link)
1400720 (Download)
1400725 (Download)
1400727 (Download)
1400804 (Download)
1400809 (Download)
1400811 (Download) See also (Link) and (Download) and (Link)
1400816 (Download) See also (Link)
1400818 (Download)
1400823 (Download)
1400825 (Download)
1400830 (Download)
1400902 (Download)
1400907 (Download)
1400909A (Download) 1400909B (Download), 1400909C (Download) Related papers for Fisher (Link), (Link), (Link)
1400914 (Download)
1400916 (Download)
1400921 (Download) a relevant Ref. (link)
1400923 (Download) a relevant Ref. (link)
1400928 (Download)
1400930 (Download)
1400105 (Download)
1400107 (Download)
14001014 (Download)
14001016 (Download)
14001021 (Download)

First midterm: 1400/09/04 Questions

Second midterm: 1400/10/09 Questions & sin.txt & CoV & Answerkey

Final exam: 1400/10/30 Questions

Marks (Download), Midterm1(Download), Midterm2(Download), Final(Download)

Exercises:

# Set 1 (Download) Necessary files (Q1-Part A) (Q2-Part B)

# Set 2 (Download) Related Data (Download)

# Set 3 (Download)

# Set 4 (Download) Related Data (Download)

# Set 5 (Download) data1 (Download) data (including 0.2.txt, 0.5.txt and 0.8.txt) (Download) (1+1)-Dimension data (Download) (1+2)-Dimension data (Download) and (Download)