MATHEMATICS 2
Instructional goals
The aim of the course is to provide working knowledge of techniques and tools of linear algebra that will be needed in the study of economics, management, finance. At the end of the course students should be able to solve problems adapting all the techniques learned and to discuss the importance of the main theoretical results presented during the course.
Intended learning outcomes
Knowledge and understanding:
The course will provide many basic mathematical tools, together with their theoretical interpretation and with examples of their possible applications to quantitative analysis in Economics, Finance, Statistics, with the help of the MATLAB software when needed.
Applying knowledge and understanding:
Students will be able to adapt and apply a variety of techniques to solve non-standard mathematical problems, as well as to discuss the arguments that justify usage of such techniques.
Making judgements:
Students are expected to be able to choose properly the best solution strategy for each mathematical problem and to understand how to apply concepts and tools to economics and financial problems. This ability will be evaluated via exercises and exams.
Communications Skills:
Students will learn how to properly formulate and communicate mathematical concepts and logical reasoning, using the English language. They will also understand how different concrete problems can be studied using similar techniques.
Learning skills:
Students will broaden their mathematical knowledge and reasoning and they will become able to work independently with advanced mathematical concepts and tools; they will also learn basics of the MATLAB language.
Course Contents
An introduction to linear algebra, with applications to economics.
Matrices, Systems of Linear Equations, Vector Spaces, Subspaces and Bases, Eigenvalues and Eigenvectors, Orthogonality, Least-squares, linear programming.
Reference Books
Main text: R.Larson, Elementary Linear Algebra, 8th edition, Cengage
Other required readings:
D.C. Lay, S.R. Lay and J.J. McDonald, Linear Algebra and Its Applications, 5th Edition, Pearson.
S.J. Leon, Linear Algebra with Applications, 8th Edition, Pearson.
K.Sydsaeter, P.J.Hammond, A.Strom, A.Carvajal, Essential Mathematics for Economic Analysis, 6th Edition, Pearson.
W.H.Green, Econometric Analysis, 8th Edition, Pearson
Teaching Methods
Lectures on the main theoretical aspects of each topic, with examples and short exercises.
Weekly exercise sessions: written exercises + MatLab. The study material is given to the students some days before the session.
Assessment Method
Students will be evaluated through a written and an oral exam. Each written exam will consist of quizzes and written exercises.
Students are required to solve exercises with suitable explanations and arguments, proving knowledge of the necessary instruments and using proper mathematical language.
The written exam is sufficient with a grade of 18 or higher and gives aces to the oral exam.
In place of the final written exam, students may take two midterm exams during the course. Each midterm exam is passed with a grade of 18 or higher and the grade for the written exam is given by the average of the two midterm exams.
The oral exam is compulsory for students who get a grade of 27 or higher. The students in this situation who do not take the oral exam will have a final grade of 26.
The oral exam will also be required upon request from the teacher. All the students who obtained a grade between 18 and 26 and who are not asked by the teacher to sit for the oral exam can register the grade for the written exam by stating their intention to register the grade on a questionnaire tool of the course’s Moodle page. In order to register the exam students must book the written and the oral exam in all cases
Students who participate to the oral exam will be given a matlab exercise the day prior to the exam. Students must complete the assignment and they must be prepared to discuss it in the oral exam. The oral exam may then continue with a discussion of all the topics described in class and contained in the syllabus. In particular, the following may be asked: definitions, properties, descriptions, examples, and mathematical exercises. The final grade can be higher or lower than the grade obtained in the written exam.
Students who obtained a grade between 16 and 18 in the written exam can take the oral exam. No assignment will be given to them, but they must demonstrate a good command of the subject, better than that resulting from the written exam.
Thesis assignment criteria
Interview with the instructor.
Week 1
1)Systems of linear equations
2) Gauss and Gauss-Jordan Elimination. (Larson Chapter 1)
TA sessions on the lectures of the present week.
Week 2
Matrices.
1) Operations and Properties of matrices.
2) The Inverse of a Matrix.
3) Markov Chains. (Larson Chapter 2)
TA sessions on the lectures of the present week.
Week 3
Determinants.
1) The Determinant of a Matrix.
2) Properties of Determinants. (Larson Chapter 3)
3) Vector Spaces and Subspaces (Larson First part of Chapter 4)
TA sessions on the lectures of the present week.
Week 4
1) Spanning Sets and Linear Independence.
2) Basis and Dimension.
3) Rank of a Matrix and Systems of Linear Equations. (Larson: Second part of Chapter 4)
TA sessions on the lectures of the present week.
Week 5
1) Linear programs, feasibility, polyhedra and polytopes, solving a linear problem;
2) Dual linear programs, weak and strong duality theorems, complementarity conditions. (Essential, Chapter 19)
TA sessions on the lectures of the present week.
Week 6
Recap of the previous topics, Midterm exam
Week 7
Inner Product Spaces
1) Length and Dot Product in the Euclidean space 2) Inner Product Spaces (Larson, First Part of Chapter 5)
TA sessions on the lectures of the present week.
Week 8
Inner Product Spaces
1) Orthonormal Bases: Gram-Schmidt Process 2)Mathematical Models and Least Squares Analysis (Larson, Second Part of Chapter 5)
TA sessions on the lectures of the present week.
Week 9
Eigenvalues and Eigenvectors
1) Definition of Eigenvalues and Eigenvectors 2) Diagonalization (Larson, First part of Chapter 7)
TA sessions on the lectures of the present week.
Week 10
Eigenvalues and Eigenvectors
1) Symmetric Matrices, Eigenvalues and Eigenvectors 2) Orthogonal Diagonalization (Larson, Second part of Chapter 7)
TA sessions on the lectures of the present week.
Week 11
1)Principal Component Analysis (Lay, Section 7.5)
2)Eigenvalues and Markov chains (Leon, Section 6.3)
TA sessions on the lectures of the present week.
Week 12
1)Quadratic forms of definite matrices (Green, Appendix A7)
2)Differentiation and optimization of functions of several variables using matrices (Green, Appendix A8)
TA sessions on the lectures of the present week.