To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:. When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. Learn more. More questions? Visit the Learner Help Center. Math and Logic. Matrix Algebra for Engineers. Jeffrey R. Top Instructor. Enroll for Free Starts Nov Offered By. About this Course , recent views. Flexible deadlines. Shareable Certificate. Beginner Level.
Hours to complete. Available languages. What you will learn Matrices. Systems of Linear Equations. Vector Spaces. Eigenvalues and eigenvectors. Skills you will gain Linear Algebra Engineering Mathematics. Instructor rating. Chasnov Top Instructor. Offered by. The Hong Kong University of Science and Technology HKUST - A dynamic, international research university, in relentless pursuit of excellence, leading the advance of science and technology, and educating the new generation of front-runners for Asia and the world.
Week 1. Video 11 videos. Promotional Video 4m. Week One Introduction 1m. Definition of a Matrix Lecture 1 7m. Addition and Multiplication of Matrices Lecture 2 10m. Special Matrices Lecture 3 9m. Transpose Matrix Lecture 4 9m. Inner and Outer Products Lecture 5 9m.
Inverse Matrix Lecture 6 12m. Orthogonal Matrices Lecture 7 4m. Rotation Matrices Lecture 8 8m. Permutation Matrices Lecture 9 6m. Reading 25 readings. Welcome and Course Information 1m. Construct Some Matrices 5m. Matrix Addition and Multiplication 5m. Matrix Multiplication Does Not Commute 5m. Lecture 18 Homework 9 Course Sessions 20 : Show All.
Lecture 1. Lecture 2. Lecture 3. Lecture 4. Lecture 5. Lecture 6. Lecture 7. Lecture 8. Lecture 9. Lecture Cover page and table of contents. Orthonormal sets of vectors and QR factorization. Book is designed beautifully and the topics here follow the recommendations of the Linear Algebra Curriculum Study Group, which were based on a careful investigation of the real needs of the students and a consensus among professionals in many disciplines that use linear algebra.
Modern View of Matrix Multiplication — The definitions and proofs focus on the columns of a matrix rather than on the matrix entries. Early Introduction of Key Concepts — Many fundamental ideas of linear algebra are introduced within the first seven lectures, in the concrete setting of Rn, and then gradually examined from different points of view.
Their use enhances the geometric flavor of the text. The book comprises chapters on synchronization and consensus in multiagent systems. It shows that the joint presentation of synchronization and consensus enables readers to learn about similarities and differences of both concepts.
It reviews the cooperative control of multi-agent dynamical systems interconnected by a communication network topology. Using the terminology of cooperative control, each system is endowed with its own state variable and dynamics. A fundamental problem in multi-agent dynamical systems on networks is the design of distributed protocols that guarantee consensus or synchronization in the sense that the states of all the systems reach the same value.
It is evident from the results that research in multiagent systems offer opportunities for further developments in theoretical, simulation and implementations. This book attempts to fill this gap and aims at presenting a comprehensive volume that documents theoretical aspects and practical applications.
The key to this approach is to view the multi-input, multi-output MIMO plant for which one wishes to design a controller as a matrix over the fraction field F associated with a commutative ring with identity, denoted by R, which also has no divisors of zero.
In this setting, the set of single-input, single-output SISO stable control systems is precisely the ring R, while the set of stable MIMO control systems is the set of matrices whose elements all belong to R. The set of unstable, meaning not necessarily stable, control systems is then taken to be the field of fractions F associated with R in the SISO case, and the set of matrices with elements in F in the MIMO case.
The central notion introduced in the book is that, in most situations of practical interest, every matrix P whose elements belong to F can be "factored" as a "ratio" of two matrices N,D whose elements belong to R, in such a way that N,D are coprime. In the familiar case where the ring R corresponds to the set of bounded-input, bounded-output BIBO -stable rational transfer functions, coprimeness is equivalent to two functions not having any common zeros in the closed right half-plane including infinity.
However, the notion of coprimeness extends readily to discrete-time systems, distributed-parameter systems in both the continuous- as well as discrete-time domains, and to multi-dimensional systems. Thus the stable factorization approach enables one to capture all these situations within a common framework. The key result in the stable factorization approach is the parametrization of all controllers that stabilize a given plant.
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