Full Index for Slitex Foilset COMPUTATIONAL SCIENCE SIMULATION TRACK CPS615/713 SOME PRACTICAL OPTIMIZATION METHODS

Slitex Foilset COMPUTATIONAL SCIENCE SIMULATION TRACK CPS615/713 SOME PRACTICAL OPTIMIZATION METHODS

This lecture-set discusses a variety of problems including TSP, linear programming, and data analysis. We discuss relevance of central limit theorem, least squares, maximum likelihood, histograms, and scatterplots.We use simple length measurement, as well as full scale physics experiment to discuss chisq, maximum likelihood, and their contrast. Computer implementations are discussed for Nonlinear Minimization, linear programming, and histogramming. The foils end with notes for a set of CPS713 projects in this area.

1:Abstract of Optimization Presentation
2:Optimization Examples---I
3:Examples -- II
4:Examples -- III
5:Examples IV -- Linear Programming
6:Optimization Examples -- V
7:A Collection of Methods for Optimization
8:Optimization Example -- V: Analysis of Experimental Data
9:Analysis of Experimental Data (cont.)
10:Data Analysis Examples---I
11:Data Analysis Examples---II
12:The Computational and Numerical Issues
13:SSC Scenario---I
14:SSC Scenario II
15:Uncertainties in SSC Data Analysis
16:SSC Data Analysis Problem Statement
17:Maximum Likelihood Principle---Example
18:Rod Quivering in an Earthquake--II
19:Bayes Formula
20:Interpretation of Bayes Relation -- I
21:Interpretation of Bayes Relation -- II
22:Basic Likelihood Formula
23:Pictorial View of Maximum Likelihood
24:General Maximum Likelihood Method
25:Lifetime Example for Maximum Likelihood---I
26:Lifetime Example for Maximum Likelihood -- II
27:Multiplication of Experiments
28:General Maximum Likelihood
29:Method
30:Method Derived from Likelihood
31:Standard Deviation
32:Scattering Experiment Example
33:Individual Data Points
34:Histogrammed Data
35:Scattering Experiment--- Minimization
36:Formulae
37:Errors in
38:Example II -- Regression Analysis
39:Example III -- Nuclear Decay
40:Maximum Likelihood and for Experimental Data Analysis
41:When Should You Use and When Maximum Likelihood?
42:Least Squares
43:Nonlinear Minimization
44:Validity of Linearization When Does It Work?
45:Nonlinear Minimization That Works!
46:Linear Programming
47:Convex Regions and Linear Programming
48:Matrix Formulation of Linear Programming
49:Computational Issues in and Maximum Likelihood
50:Matrices in Maximum Likelihood
51:Basic Software Structure for Derivative Calculation
52:Computational Complexity
53:Histograms
54:Histogram Formalism I
55:Histogram Formalism II
56:Errors of Histograms -- I
57:Errors of Histograms -- II
58:Histograms as Robust Data Exploration
59:General Histogram
60:Scatterplots
61:Possible Projects I---Maximum Likelihood
62:Possible Projects II
63:Possible Projects II (cont.)
64:More Possible Maximum Likelihood Projects
65:Projects---Data Exploration
66:More Data Exploration Projects

67:Geoffrey Fox,

Northeast Parallel Architectures Center at Syracuse University, gcf@npac.syr.edu