Nullspace Of A Matrix(Continued)
Range Of A Matrix
Inverse
Rank Of A Matrix
Conservation Of Dimension
'Coding' Interpretation Of Rank
Application: Fast Matrix:Vector Multiplication
Change Of Coordinates
(Euclidian) Norm
Inner Product
Orthanormal Set Of Vectors
Boyd, Stephen
Stephen P. Boyd is the Samsung Professor of Engineering, and Professor of Electrical Engineering in the Information Systems Laboratory at Stanford University. His current research focus is on convex optimization applications in control, signal processing, and circuit design.
Professor Boyd received an AB degree in Mathematics, summa cum laude, from Harvard University in 1980, and a PhD in EECS from U. C. Berkeley in 1985. In 1985 he joined the faculty of Stanford’s Electrical Engineering Department. He has held visiting Professor positions at Katholieke University (Leuven), McGill University (Montreal), Ecole Polytechnique Federale (Lausanne), Qinghua University (Beijing), Universite Paul Sabatier (Toulouse), Royal Institute of Technology (Stockholm), Kyoto University, and Harbin Institute of Technology. He holds an honorary doctorate from Royal Institute of Technology (KTH), Stockholm.
Professor Boyd is the author of many research articles and three books: Linear Controller Design: Limits of Performance (with Craig Barratt, 1991), Linear Matrix Inequalities in System and Control Theory (with L. El Ghaoui, E. Feron, and V. Balakrishnan, 1994), and Convex Optimization (with Lieven Vandenberghe, 2004).
Professor Boyd has received many awards and honors for his research in control systems engineering and optimization, including an ONR Young Investigator Award, a Presidential Young Investigator Award, and an IBM faculty development award. In 1992 he received the AACC Donald P. Eckman Award, which is given annually for the greatest contribution to the field of control engineering by someone under the age of 35. In 1993 he was elected Distinguished Lecturer of the IEEE Control Systems Society, and in 1999, he was elected Fellow of the IEEE, with citation: “For contributions to the design and analysis of control systems using convex optimization based CAD tools.” He has been invited to deliver more than 30 plenary and keynote lectures at major conferences in both control and optimization.
In addition to teaching large graduate courses on Linear Dynamical Systems, Nonlinear Feedback Systems, and Convex Optimization, Professor Boyd has regularly taught introductory undergraduate Electrical Engineering courses on Circuits, Signals and Systems, Digital Signal Processing, and Automatic Control. In 1994 he received the Perrin Award for Outstanding Undergraduate Teaching in the School of Engineering, and in 1991, an ASSU Graduate Teaching Award. In 2003, he received the AACC Ragazzini Education award, for contributions to control education, with citation: “For excellence in classroom teaching, textbook and monograph preparation, and undergraduate and graduate mentoring of students in the area of systems, control, and optimization.”
Handout Name  Handout Usage 

Cover page and table of contents  
Overview  Lecture 1 
Linear functions  Lecture 23 
Linear algebra review  Lectures 34 
Orthonormal sets of vectors and QR factorization  Lecture 45 
Leastsquares  Lectures 56 
Leastsquares applications  Lectures 67 
Regularized leastsquares and GaussNewton method  Lectures 78 
Leastnorm solutions of underdetermined equations  Lectures 89 
Autonomous linear dynamical systems  Lecture 9 
Solution via Laplace transform and matrix exponential  Lecture 11 
Eigenvectors and diagonalization  Lectures 1213 
Jordan canonical form  Lectures 1314 
Linear dynamical systems with inputs and outputs  Lectures 1415 
Example: Aircraft dynamics  Lecture 15 
Symmetric matrices, quadratic forms, matrix norm, and SVD  Lectures 1517 
SVD applications  Lectures 1718 
Example: Quantum mechanics  Lecture 18 
Controllability and state transfer  Lectures 1820 
Observability and state estimation  Lecture 20 
Summary and final comments  Lecture 20 
All numbered exercises are from the EE263 homework problems.
You will sometimes need to download Matlab files, see Software below.
Assignment  Exercises  Due Date 

Homework 1  2.1–2.4, 2.6, 2.9, 2.12, and an additional exercise  Lecture 4 
Homework 2  3.2, 3.3, 3.10, 3.11, 3.16, 3.17, and three additional exercises  Lecture 6 
Homework 3  2.17, 3.13, 4.1–4.3, 5.1, 6.9, and two additional exercises  Lecture 8 
Homework 4  5.2, 6.2, 6.5, 6.12, 6.14, 6.26, 7.3, 8.2  Lecture 10 
Homework 5  10.2, 10.3, 10.4, and an additional exercise  Lecture 13 
Homework 6  9.9, 10.5, 10.6, 10.8, 10.14, 11.3, and 11.6a  Lecture 14 
Homework 7  10.9, 10.11, 10.19, 11.13, 12.1, 13.1, and an additional exercise  Lecture 16 
Homework 8  13.17, 14.2, 14.3, 14.4, 14.6, 14.8, 14.9, 14.11, 14.13, 14.21, 14.33, and an additional exercise Please note: (a) you have two weeks to do these, and (b) some of them are very straightforward. 
Lecture 18 
Homework 9  14.16, 14.26, 15.2, 15.3, 15.6, 15.8, 15.10, and 15.11  Lecture 20 
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Topics: Linear Functions (Continued), Interpretations Of Y=Ax, Linear Elastic Structure, Example, Total Force/Torque On Rigid Body Example, Linear Static Circuit Example, Illumination With Multiple Lamps Example, Cost Of Production Example, Network Traffic And Flow Example, Linearization And First Order Approximation Of Functions 
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Topics: Linearization (Continued), Navigation By Range Measurement, Broad Categories Of Applications, Matrix Multiplication As Mixture Of Columns, Block Diagram Representation, Linear Algebra Review, Basis And Dimension, Nullspace Of A Matrix 
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Topics: Orthonormal Set Of Vectors, Geometric Interpretation, GramSchmidt Procedure, General GramSchmidt Procedure, Applications Of GramSchmidt Procedure, 'Full' QR Factorization, Orthogonal Decomposition Induced By A, LeastSquares 
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Topics: LeastSquares, Geometric Interpretation, LeastSquares (Approximate) Solution, Projection On R(A), LeastSquares Via QR Factorization, LeastSquares Estimation, Blue Property, Navigation From Range Measurements, LeastSquares Data Fitting 
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Topics: LeastSquares Polynomial Fitting, Norm Of Optimal Residual Versus P, LeastSquares System Identification, Model Order Selection, CrossValidation, Recursive LeastSquares, MultiObjective LeastSquares 
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Topics: MultiObjective LeastSquares, WeightedSum Objective, Minimizing WeightedSum Objective, Regularized LeastSquares, Laplacian Regularization, Nonlinear LeastSquares (NLLS), GaussNewton Method, GaussNewton Example, LeastNorm Solutions Of Undetermined Equations 
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Topics: LeastNorm Solution, LeastNorm Solution Via QR Factorization, Derivation Via Langrange Multipliers, Example: Transferring Mass Unit Distance, Relation To Regularized LeastSquares, General Norm Minimization With Equality Constraints, Autonomous Linear Dynamical Systems, Block Diagram 
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Topics: Examples Of Autonomous Linear Dynamical Systems, FiniteState DiscreteTime Markov Chain, Numerical Integration Of Continuous System, High Order Linear Dynamical Systems, Mechanical Systems, Linearization Near Equilibrium Point, Linearization Along Trajectory 
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Topics: Solution Via Laplace Transform And Matrix Exponential, Laplace Transform Solution Of X_^ = Ax, Harmonic Oscillator Example, Double Integrator Example, Characteristic Polynomial, Eigenvalues Of A And Poles Of Resolvent, Matrix Exponential, Time Transfer Property 
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Topics: Time Transfer Property, Piecewise Constant System, Qualitative Behavior Of X(T), Stability, Eigenvectors And Diagonalization, Scaling Interpretation, Dynamic Interpretation, Invariant Sets, Summary, Markov Chain (Example) 
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Topics: Markov Chain (Example), Diagonalization, Distinct Eigenvalues, Digaonalization And Left Eigenvectors, Modal Form, Diagonalization Examples, Stability Of DiscreteTime Systems, Jordan Canonical Form, Generalized Eigenvectors 
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Topics: Jordan Canonical Form, Generalized Modes, CayleyHamilton Theorem, Proof Of CH Theorem, Linear Dynamical Systems With Inputs & Outputs, Block Diagram, Transfer Matrix, Impulse Matrix, Step Matrix 
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Topics: DC Or Static Gain Matrix, Discretization With Piecewise Constant Inputs, Causality, Idea Of State, Change Of Coordinates, ZTransform, Symmetric Matrices, Quadratic Forms, Matrix Nom, And SVD, Eigenvalues Of Symmetric Matrices, Interpretations Of Eigenvalues Of Symmetric Matrices, Example: RC Circuit 
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Topics: RC Circuit (Example), Quadratic Forms, Examples Of Quadratic Form, Inequalities For Quadratic Forms, Positive Semidefinite And Positive Definite Matrices, Matrix Inequalities, Ellipsoids, Gain Of A Matrix In A Direction, Matrix Norm, Properties Of Matrix Norm 
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Topics: Gain Of A Matrix In A Direction, Singular Value Decomposition, Interpretations, Singular Value Decomposition (SVD) Applications, General PseudoInverse, PseudoInverse Via Regularization, Full SVD, Image Of Unit Ball Under Linear Transformation, SVD In Estimation/Inversion, Sensitivity Of Linear Equations To Data Error 
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Topics: Sensitivity Of Linear Equations To Data Error, Low Rank Approximations, Distance To Singularity, Application: Model Simplification, Controllability And State Transfer, State Transfer, Reachability, Reachability For DiscreteTime LDS 
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Topics: ContinuousTime Reachability, General State Transfer, Observability And State Estimation, State Estimation Set Up, State Estimation Problem, Observability Matrix, LeastSquares Observers, Some Parting Thoughts..., Linear Algebra, Levels Of Understanding, What's Next 