Instructor: Hemanshu Kaul
Office: 125C Rettaliata Engg Center.
E-mail: kaul [at] iit.edu

Time: 1:50-3:05pm Monday and Wednesday.
Place: 121 Rettaliata Engg Center

Discussion Forums: Math 380 on Canvas.

Office Hours: Monday and Wednesday at 4:30-5:30pm. And by appointment in-person or through Zoom (send email to setup appointment).
Questions through Canvas Discussion Forums are strongly encouraged.

TA Office Hours: Alaittin Kirtisoglu, Monday 9:30-11:30am, and Wednesday 12:45-1:45pm, at RE 129 or at Math Tutoring Center.

ARC Tutoring Service: Mathematics tutoring at the Academic Resource Center.

• Thursday, 1/16 : Note the change in classroom to RE 121


• Monday, 1/13 : Check this webpage regularly for homework assignments, announcements, etc.

• Exam #1 : 2/26 Wednesday. Syllabus: Based on topics, examples, applications corresponding to HWs #1-#5.
• Exam #2 : 4/16 Wednesday. Syllabus: Based on topics, examples, applications corresponding to HWs #6-#10.
• Final Exam : TBA. Syllabus: All topics covered during the semester.

• Week #1 : 2 Lectures.
  
  • Topics:Discussion of class structure and purpose. The process of math modeling - discussion with examples, Principle of proportionality, difference equations - examples from accounting/ finance/ science, discrete time vs. continuous time, limiting behavior of DDS(discrete dynamical system) and example of modeling births/deaths/resources through non-linear discrete dynamical systems. (From Sections 1.0, 1.1, 1.2, and elsewhere)
    
  • Lecture Notes: Outlines of lectures without all the details as discussed in the classroom. Notes#1.
  • Homework:
    The homeworks listed below are from the course textbook, Giordano, Fox, Horton, A First Course in Mathematical Modeling, 5th edition.
    You are required to follow the detailed instructions and rules for HWs given in the Course Information Handout and through Canvas and email comments.
    Work on the HW problems before and over the weekend so that you ask for help on Canvas forums or in-person with the instructor or the TA during office hours on Monday and Wednesday.
    • Reading HW: Read and understand all Examples from Sections 1.1 and 1.2.
    • HW#1 for Submission. Due Wednesday, 1/22, by 11pm in Canvas. Submit a single PDF file through Canvas Assignment.
      Section 1.1: #3bc, #10, (#12a and #13a).
      Section 1.2: Submit any two of the following three sets of problems: #2, (#6 and #7), #9.
      Questions? Ask on Canvas. Or, send email.
      HW Solutions distributed in class.


• Week #2 : 1 Lecture and 1 Holiday.
  
  • Topics: Drug dosage model and its analysis, (stable and unstable) Equilibrium values/fixed points and solutions of DDS, Solutions methods and stability of equilibrium values of homogenous and nonhomogenous linear DDS, Interacting discrete dynamical systems via a interacting species population model (Competitive Hunter model). (From Sections 1.3, 1.4)
    
  • Lecture Notes: Outlines of lectures without all the details as discussed in the classroom. Notes#2.
  • Homework:
    The homeworks listed below are from the course textbook, Giordano, Fox, Horton, A First Course in Mathematical Modeling, 5th edition.
    You are required to follow the detailed instructions and rules for HWs given in the Course Information Handout and through Canvas and email comments.
    Work on the HW problems before and over the weekend so that you ask for help on Canvas forums or in-person with the instructor or the TA during office hours on Monday and Wednesday.
    • Reading HW: Read and understand Examples from Sections 1.3 and 1.4.
    • HW#2 for Submission. Due Wednesday, 1/29, by 11pm in Canvas. Submit a single PDF file through Canvas Assignment.
      Section 1.3: #1f, #2e, #3a, #6, #14ad.
      Section 1.4: #2, #4.
      Questions? Ask on Canvas. Or, send email.
      HW Solutions distributed in class.


• Week #3 : 2 Lectures.
  
  • Topics: Proportionality in non-linear or translated linear systems -examples from physics, Geometric similarity - relationship between geometric notions like volume, surface area, etc. in terms of a fundamental dimension; Geometric similarity - Examples from physics (raindrops) and biology (fish); short discussion of SIR epidemic model; Strategy analysis using System of DDS in an Astronaut spacecraft docking procedure and its analysis with interpretation. Model fitting vs. data fitting/ Interpolation. (From Sections 2.2, 2.3, 1.4 and elsewhere)
    
  • Lecture Notes: Outlines of lectures without all the details as discussed in the classroom. Notes#3.
  • Homework:
    The homeworks listed below are from the course textbook, Giordano, Fox, Horton, A First Course in Mathematical Modeling, 5th edition.
    You are required to follow the detailed instructions and rules for HWs given in the Course Information Handout and through Canvas and email comments.
    Work on the HW problems before and over the weekend so that you ask for help on Canvas forums or in-person with the instructor or the TA during office hours on Monday and Wednesday.
    • Reading HW: Read and understand Examples from Sections 2.2, 2.3, 2.4, and 3.1. Think about Section 2.3 Problem #9 (main question, not parts ab).
    • HW#3 for Submission. Due Wednesday, 2/5, by 11pm in Canvas. Submit a single PDF file through Canvas Assignment.
      Section 2.2: submit one of (#6 OR #12).
      Section 2.3: #4, Project#2.
      Section 3.1: submit one of (#5 OR #7). [Comment: Look at Table 3.1 and 3.2 and the discussion in-between them (we will also discuss this in the next lecture). Explain the use of appropriate data transformation, and just roughly estimate (using your computational software if you wish) the values of the parameters from the plots.]
      Questions? Ask on Canvas. Or, send email.
      HW Solutions distributed in class.


• Week #4 : 2 Lectures.
  
  • Topics: Model fitting vs. data fitting/ Interpolation; Sources of qualitative and quantitative errors in the modeling process, Transforming data to fit linear systems. What is error for a collection of data points vs. a model? Fitting a model to data as minimizing appropriate l_p distance between vector of observations and vector of predictions. Chebyshev Approximation Criterion - min max absolute deviation as a linear optimization problem. Minimizing average deviation. Least squares criterion - min sum of squares of deviations, Applying calculus to find best fit model in LSC, Normal equations and critical points in LSC plus application of second derivative test, Using LSC for fitting a straight line, Fitting a power curve with fixed exponent, fitting a power curve with unknown exponent, Transforming non-linear data. Comparing various models for the same set of observed data with LSC and with CAC, Error calculations. (From Sections 3.1, 3.2, 3.3, 3.4, and elsewhere)
    
  • Lecture Notes: Outlines of lectures without all the details as discussed in the classroom. Notes#4.
  • Homework:
    The homeworks listed below are from the course textbook, Giordano, Fox, Horton, A First Course in Mathematical Modeling, 5th edition.
    You are required to follow the detailed instructions and rules for HWs given in the Course Information Handout and through Canvas and email comments.
    Work on the HW problems before and over the weekend so that you ask for help on Canvas forums or in-person with the instructor or the TA during office hours on Monday and Wednesday.
    • Reading HW: Read and understand Examples 1 and 2 from Section 3.4.
    • HW#4 for Submission. Due Wednesday, 2/12, by 11pm in Canvas. Submit a single PDF file through Canvas Assignment.
      Section 3.2: #2b, #3.
      Section 3.3: Submit one of (#4 OR #8).
      Section 3.4: #7ab, #8(compare all 4 models from #7 and #8 here, as we did in the lecture).
      [Comment: When solving the problems for Chebyshev Approximation Criterion, it is expected that your solution explicitly includes the linear optimization problem that has to be solved to apply CAC. After writing the linear optimization problem, you should solve it using any solver. For example, you can use: MATLAB : LinProg; MATHEMATICA - I; MATHEMATICA - II; Python; OTHER SOLVERS.]
      Questions? Ask on Canvas. Or, send email.
      HW Solutions distributed in class.


• Week #5 : 2 Lectures.
  
  • Topics: Optimization based Decision making, general form of an optimization problem, Introduction to Linear Optimization through variants of a model, Integer, Mixed Integer and binary optimization problems, multiobjective problems and how to convert them into single objective through three approaches, Binary programs and using 0-1 variables to make decisions with various constraints, Knapsack Problem and budgeting problems, modeling dependent "yes/no" decisions, approximating a non-linear function by a piecewise linear function using 0-1 variables. Geometric solutions of Linear programs, Geometric and linear algebraic intuition behind simplex algorithm, Local search algorithms - underlying concepts like neighborhood and step-size; thinking of simplex algorithm, optimization from Calculus as local search algorithms. (From Sections 7.1, 7.2, 7.3, and elsewhere)
    
  • Lecture Notes: Outlines of lectures without all the details as discussed in the classroom. Notes#5.
  • Homework:
    The homeworks listed below are from the course textbook, Giordano, Fox, Horton, A First Course in Mathematical Modeling, 5th edition.
    You are required to follow the detailed instructions and rules for HWs given in the Course Information Handout and through Canvas and email comments.
    Work on the HW problems before and over the weekend so that you ask for help on Canvas forums or in-person with the instructor or the TA during office hours on Monday and Wednesday.
    • Reading HW: Read and understand Examples 1 and 3 from Section 7.1. And Examples 1 and 2 from Section 7.2.
    • Homework #5 for submission [PDF]. Due Wednesday, 2/19, by 11pm in Canvas. Submit a single PDF file through Canvas Assignment.
      Questions? Ask on Canvas. Or, send email.
      HW Solutions distributed in class.


• Weeks #6 and #7 : 3 Lectures and 1 Mid-term Exam.
  
  • Topics: Local search algorithms - underlying concepts like neighborhood and step-size; thinking of simplex algorithm, optimization from Calculus as local search algorithms. Graphs and networks - basic concepts such as visual and algebraic representations, examples of vertices and edge relations from Math, Computer Science, Operations Research, Network Science, Data Science, Biology, Epidemiology, etc. Conflict-free allocation of scarce resources as Graph Coloring and related examples of scheduling/ allocation of resources, Proper coloring and chromatic number of a graph, Chromatic number of a graph as 0-1 Linear optimization problem, Greedy Algorithms, Greedy coloring for proper coloring of a graph, Monitoring a network model as a vertex cover problem, Greedy algorithm for vertex cover, Vertex Cover problem modeled as a 0-1 linear optimization problem.
    Maximum Flow problem on networks as a linear optimization problem, Modeling Matching problem as a max flow problem, Min cost transportation problem for logistics as a linear program, Eulerian graphs and Eulerian tours as a model for Konigsberg bridge problem. (From Sections 8.1, 8.2, 8.3, 8.5 and elsewhere)
    
  • Lecture Notes: Outlines of lectures without all the details as discussed in the classroom. Notes#6 and Notes#7 and Notes#8.
  • Homework:
    The homeworks listed below are from the course textbook, Giordano, Fox, Horton, A First Course in Mathematical Modeling, 5th edition.
    You are required to follow the detailed instructions and rules for HWs given in the Course Information Handout and through Canvas and email comments.
    Work on the HW problems before and over the weekend so that you ask for help on Canvas forums or in-person with the instructor or the TA during office hours on Monday and Wednesday.
    • Reading HW: Read and understand the examples in Sections 8.1, 8.3, and 8.5.
    • Homework #6 for submission [PDF]. [Based on 3+ lectures]. Due Wednesday, 3/5, by 11pm in Canvas. Submit a single PDF file through Canvas Assignment.
      Questions? Ask on Canvas. Or, send email.
      

• M.M.Meerschaert, Mathematical Modeling, Fourth Edition.
• H.P. Williams, Model Building in Mathematical Programming, Fifth Edition.
• Hillier and Lieberman, Introduction to Operations Research, 7th edition onwards.


• Wikipedia on Math Models
• OR Models


• The Unreasonable Effectiveness of Mathematics in the Natural Sciences, a famous 1960 article by Eugene Wigner, a Nobel prize winning physicist.


• For a bit of insight into what randomness means and one way of characterizing a random sequence of numbers, read through this short essay: Probability vs Randomness.


• Chasing the Elusive Numbers That Define Epidemics
• Hard Lessons of Modeling the Coronavirus Pandemic
• Mathematicians and Blue Crabs
• Ecologists nightmare is a mathematicians dream
• Universal Geometry of Geology
• Modeling Brains: Ignore the Right Details
• Equation-Free Prediction in Ecological models
• Nature's Critical Warning System
• A Mathematical Model Unlocks the Secrets of Vision
• How Nature Defies Math in Keeping Ecosystems Stable
• Evidence is only as good as the model, and modeling can be dangerous business. So how much evidence is enough?
• Universal Law of Turbulence
• A Statistical Search for Genomic Truths
• All Change Is a Mix of Order and Randomness

• You can access MATLAB through the IIT's Virtual Computing Lab (VCL) which is accessible through your my.iit.edu account.
  See FAQs regarding VCL.
  Refer to VCL Instructions here.
• MATLAB Overview
• Getting Started with MATLAB
• MATLAB Development Environment
• Preface of Moler's Textbook
• Chapter 1 of Moler's Textbook
• Single Page summary of essential MATLAB commands
• Twelve Page summary of essential MATLAB commands
• Another Introduction to MATLAB from a more advanced book of Moler.

• Mathematica at IIT
• Mathematica Tutorials - Videos.
• Mathematica Tutorials.
• A fast introduction to Mathematica for Programmers.
• An elementary introduction to Mathematica.
• Introduction to Mathematica.