This document contains notes from a lesson on determinants and inverses. It defines determinants as the sum of products of matrix elements with one element from each row and column, and lists several rules for how determinants change with row/column operations, including that the determinant is 0 if rows/columns are proportional or duplicated. It also states the determinant of a product of matrices is the product of the individual determinants.
Numerical solution of eigenvalues and applications 2SamsonAjibola
This project aims at studying the methods of numerical solution of eigenvalue problems and their applications. An accurate mathematical method is needed to solve direct and inverse eigenvalue problems related to different applications such as engineering analysis and design, statistics, biology e.t.c. Eigenvalue problems are of immense interest and play a pivotal role not only in many fields of engineering but also in pure and applied mathematics, thus numerical methods are developed to solve eigenvalue problems. The primary objective of this work is to showcase these various eigenvalue algorithms such as QR algorithm, power method, Krylov subspace iteration (Lanczos and Arnoldi) and explain their effects and procedures in solving eigenvalue problems.
The Engineer of Industrial Universtiy of Santander, Elkin Santafe, give us a little summary about direct methods for the solution of systems of equations
Numerical solution of eigenvalues and applications 2SamsonAjibola
This project aims at studying the methods of numerical solution of eigenvalue problems and their applications. An accurate mathematical method is needed to solve direct and inverse eigenvalue problems related to different applications such as engineering analysis and design, statistics, biology e.t.c. Eigenvalue problems are of immense interest and play a pivotal role not only in many fields of engineering but also in pure and applied mathematics, thus numerical methods are developed to solve eigenvalue problems. The primary objective of this work is to showcase these various eigenvalue algorithms such as QR algorithm, power method, Krylov subspace iteration (Lanczos and Arnoldi) and explain their effects and procedures in solving eigenvalue problems.
The Engineer of Industrial Universtiy of Santander, Elkin Santafe, give us a little summary about direct methods for the solution of systems of equations
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Integration by substitution is the chain rule in reverse.
NOTE: the final location is section specific. Section 1 (morning) is in SILV 703, Section 11 (afternoon) is in CANT 200
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Lesson 24: Areas and Distances, The Definite Integral (slides)Matthew Leingang
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At times it is useful to consider a function whose derivative is a given function. We look at the general idea of reversing the differentiation process and its applications to rectilinear motion.
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Uncountably many problems in life and nature can be expressed in terms of an optimization principle. We look at the process and find a few good examples.
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Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
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Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
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All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
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UI automation Sample
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Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Designing Great Products: The Power of Design and Leadership by Chief Designe...
Lesson 8: Determinants III
1. Lesson 8
Determinants and Inverses (Section 13.5–6)
Math 20
October 5, 2007
Announcements
No class Monday 10/8, yes class Friday 10/12
Problem Set 3 is on the course web site. Due October 10
Sign up for conference times on course website
Prob. Sess.: Sundays 6–7 (SC 221), Tuesdays 1–2 (SC 116)
OH: Mondays 1–2, Tuesdays 3–4, Wednesdays 1–3 (SC 323)
2. Review: Determinants of n × n matrices by patterns
Definition
Let A = (aij )n×n be a matrix. The determinant of A is a sum of
all products of n elements of the matrix, where each product takes
exactly one entry from each row and column.
3. Review: Determinants of n × n matrices by patterns
Definition
Let A = (aij )n×n be a matrix. The determinant of A is a sum of
all products of n elements of the matrix, where each product takes
exactly one entry from each row and column.
The sign of each product is given by (−1)σ , where σ is the number
of upwards lines used when all the entries in a pattern are
connected.
6. Determinants of n × n matrices by cofactors
Definition
Let A = (aij )n×n be a matrix. The (i, j)-minor of A is the matrix
obtained from A by deleting the ith row and j column. This matrix
has dimensions (n − 1) × (n − 1).
The (i, j) cofactor of A is the determinant of the (i, j) minor
times (−1)i+j .
9. Example
2 −4 3
Compute the determinant: 3 1 2
1 4 −1
Expand along 1st row
Expand along 2nd row
Expand along 1st column
10.
11. Fact
The determinant of A = (aij )n×n is the sum
a11 C11 + a12 C12 + · · · + a1n C1n
Fact
The determinant of A = (aij )n×n is the sum
a11 Ci1 + ai2 Ci2 + · · · + ain Cin
for any i.
12. Fact
The determinant of A = (aij )n×n is the sum
a11 C11 + a12 C12 + · · · + a1n C1n
Fact
The determinant of A = (aij )n×n is the sum
a11 Ci1 + ai2 Ci2 + · · · + ain Cin
for any i.
Fact
The determinant of A = (aij )n×n is the sum
a1j C1j + a2j C2j + · · · + anj Cnj
for any j.
14. Theorem (Rules for Determinants)
Let A be an n × n matrix.
1. If a row or column of A is full of zeros, then |A| =
15. Theorem (Rules for Determinants)
Let A be an n × n matrix.
1. If a row or column of A is full of zeros, then |A| = 0.
16. Theorem (Rules for Determinants)
Let A be an n × n matrix.
1. If a row or column of A is full of zeros, then |A| = 0.
2. |A | =
17.
18.
19. Theorem (Rules for Determinants)
Let A be an n × n matrix.
1. If a row or column of A is full of zeros, then |A| = 0.
2. |A | = |A|
20.
21. Theorem (Rules for Determinants)
Let A be an n × n matrix.
1. If a row or column of A is full of zeros, then |A| = 0.
2. |A | = |A|
3. If B is the matrix obtained by multiplying each entry of one
row or column of A by the same number α, then |B| = α |A|.
22. Theorem (Rules for Determinants)
Let A be an n × n matrix.
1. If a row or column of A is full of zeros, then |A| = 0.
2. |A | = |A|
3. If B is the matrix obtained by multiplying each entry of one
row or column of A by the same number α, then |B| = α |A|.
4. If two rows or columns of A are interchanged,
23.
24. Theorem (Rules for Determinants)
Let A be an n × n matrix.
1. If a row or column of A is full of zeros, then |A| = 0.
2. |A | = |A|
3. If B is the matrix obtained by multiplying each entry of one
row or column of A by the same number α, then |B| = α |A|.
4. If two rows or columns of A are interchanged, then the
determinant changes its sign but keeps its absolute value.
25. Theorem (Rules for Determinants)
Let A be an n × n matrix.
1. If a row or column of A is full of zeros, then |A| = 0.
2. |A | = |A|
3. If B is the matrix obtained by multiplying each entry of one
row or column of A by the same number α, then |B| = α |A|.
4. If two rows or columns of A are interchanged, then the
determinant changes its sign but keeps its absolute value.
5. If a row or column of A is duplicated, then |A| =
26.
27. Theorem (Rules for Determinants)
Let A be an n × n matrix.
1. If a row or column of A is full of zeros, then |A| = 0.
2. |A | = |A|
3. If B is the matrix obtained by multiplying each entry of one
row or column of A by the same number α, then |B| = α |A|.
4. If two rows or columns of A are interchanged, then the
determinant changes its sign but keeps its absolute value.
5. If a row or column of A is duplicated, then |A| = 0.
29. Theorem (Rules for Determinants, continued)
Let A be an n × n matrix.
5. If a row or column of A is proportional to another, then
|A| =
30. Theorem (Rules for Determinants, continued)
Let A be an n × n matrix.
5. If a row or column of A is proportional to another, then
|A| = 0.
31. Theorem (Rules for Determinants, continued)
Let A be an n × n matrix.
5. If a row or column of A is proportional to another, then
|A| = 0.
6. If a scalar multiple of one row (or column) of A is added to
another row (or column), then
32.
33. Theorem (Rules for Determinants, continued)
Let A be an n × n matrix.
5. If a row or column of A is proportional to another, then
|A| = 0.
6. If a scalar multiple of one row (or column) of A is added to
another row (or column), then the determinant does not
change.
34. Theorem (Rules for Determinants, continued)
Let A be an n × n matrix.
5. If a row or column of A is proportional to another, then
|A| = 0.
6. If a scalar multiple of one row (or column) of A is added to
another row (or column), then the determinant does not
change.
7. The determinant of the product of two matrices is the product
of the determinants of those matrices:
|AB| = |A| |B|
35. Theorem (Rules for Determinants, continued)
Let A be an n × n matrix.
5. If a row or column of A is proportional to another, then
|A| = 0.
6. If a scalar multiple of one row (or column) of A is added to
another row (or column), then the determinant does not
change.
7. The determinant of the product of two matrices is the product
of the determinants of those matrices:
|AB| = |A| |B|
8. if α is any real number, then
36. Theorem (Rules for Determinants, continued)
Let A be an n × n matrix.
5. If a row or column of A is proportional to another, then
|A| = 0.
6. If a scalar multiple of one row (or column) of A is added to
another row (or column), then the determinant does not
change.
7. The determinant of the product of two matrices is the product
of the determinants of those matrices:
|AB| = |A| |B|
8. if α is any real number, then |αA| =
37. Theorem (Rules for Determinants, continued)
Let A be an n × n matrix.
5. If a row or column of A is proportional to another, then
|A| = 0.
6. If a scalar multiple of one row (or column) of A is added to
another row (or column), then the determinant does not
change.
7. The determinant of the product of two matrices is the product
of the determinants of those matrices:
|AB| = |A| |B|
8. if α is any real number, then |αA| = αn |A|.