You see inequalities in any discipline. In math-fin area specifically, most of the times, the inequalities are derived from a basic set of inequalities such as Cauchy’s, AM-GM, Jensen’s,Holder’s, Minkowski’s Inequalities. One typically comes across inequalities in some math course where one is asked to prove a specific inequality. Typically one solves them just like one learns the grammar and syntax of a language. Sadly ,the principles behind them are conveniently forgotten as one does not actually use then in some practical context. So, the GAP between the time one is working on basic inequalities in a course work and the time where one needs to apply them to solve a practical problem, sometimes runs in to years/ decades. Sadly , all one can do is to go back and refer some books on inequalities that most often than not , appear consisting of pages with a laundry list of inequalities. You start to wonder, “ Are there any principles behind solving inequalities?”, “Are there any common strategies to work with inequalities ?” , “Is there a connection between a specific strategy / inequality to problems in various scientific disciplines ?”. The book provides these answers in a delightful way. Math books written in conversational style are few and this is one of them.
The word , “Master Class”, in title of the book attracted me to this book, as I had come across this term and the context in “Shop class as Soul craft”.The author at the very beginning of this book says :
In the fine arts, a master class is a small class where students and coaches work together to support a high level of technical and creative excellence.This book tries to capture the spirit of a master class while providing coaching for readers who want to refine their skills as solvers of problems, especially those problems dealing with mathematical inequalities.
The book starts off with the famous Cauchy’s Inequality. The beautiful thing about inequalities is that it will give you a chance to view them from different lens. You can wear a linear algebra lens OR inner product vector space lens OR a simple algebraic lens OR functional analysis lens etc. I mean whatever your area of work, you would have come across some variant of Cauchy inequality. When I first saw the inequality, I drew parallels to two processes which have finite variance and the inequality as the link between covariance of such processes and the individual variance. So, in one way, by studying inequalities you get to view stuff from various perspectives. You never know which perspective would help you to solve a problem in your area of work. Coming to the chapter on Cauchy’s inequality, the author proves it by induction. Then the author talks about a very important principle that one must keep in mind.
Mathematical progress depends on the existence of a continuous stream of new problems, yet the processes that generate such problems may seem mysterious. To be sure, there is genuine mystery in any deeply original problem, but most new problems evolve quite simply from well established principles. One of the most productive of these principles calls on us to expand our understanding of a quantitative result by first focusing on its qualitative inferences.
The above statement appears VAGUE until you see the connection between Cauchy inequality and
The latter statement is an inference from Cauchy’s inequality but when applied systematically gives rise to to an additive inequality
The chapter then introduces the concept of inner product space to look at the same inequality. It shows that the inequality is nothing but an expression in inequality for the cosine of angle between two vectors in an inner product vector space.
Moving from the discrete version of the inequality to continuous form took about 6 years and it appeared in a memoir written by Victor Yacovlevich Bunyakovsky. There was no proof given to the continuous form of inequality
The continuous version of the inequality was proved brilliantly by Hermann Amandus Schwarz (1843–1921) while working on the theory of minimal surfaces. He also came out with inner product version of the inequality. One brilliant point of this chapter is the derivation of Cramer Rao bound for the MLE problem using Cauchy’s inequality. Any guy who uses stats in his work will appreciate the derivation of this bound from a simple looking Cauchy’s inequality. This introductory chapter introduces 1-Trick and Splitting-Trick that are often used in the context of proving Cauchy’s variants.
My biggest takeaway from this chapter has nothing to do with inequalities but it is about learning a method to crank out inner product spaces. I used to always wonder whenever I came across an inner product definition , about its source .”How do people come up with the inner product definition which just satisfies all the properties of inner product ?”. From this chapter, I have learnt that the source is any positive definite matrix. If you take any positive definite matrix A, then for any two vectors x and y in the vector space, you can define inner product as Y transpose times A times X and that will get you an inner product definition.
< The AM-GM Inequality >
The next type of inequalities the book focuses on are Arithmetic Mean – Geometric Mean inequality.
This is one of the most popular inequalities. It is learnt in some form or the other either in high school/ college level/ undergrad education. The proof is usually shown by mathematical induction. However the book goes on to prove using a technique “ Leap-Forward Fall-Back technique”, a technique which works in lots of cases , where the usage of mathematical induction might be tedious.
Self generalizing quality of the above inequality is used to explain Generalized AM-GM inequality
One of the challenging questions before proving any inequality is , “What elementary inequalities should be used ?”. Just because something looks like a AM-GM inequality does not mean you can use raw version of AM-GM inequality and proceed. An example of this is shown using Carleman’s inequality where the usual AM-GM fails to go anywhere. The example illustrates the principle of maximal effectiveness whereby we conspire to use our tools under precisely those circumstances when they are at their best. The chapter ends with stating the three principles from George Polya which are extremely relevant for solving inequalities
Can you solve your problem in a special case?
Can you relate your problem to a similar one where the answer is already known?
Can you compute anything at all that is related to what you would really like to compute?
< Lagrange’s Identity and Minkowski’s Conjecture >
The author then sheds light on Lagrange’s Identity and Minkowski’s conjecture. The connection between Cauchy’s inequality and Lagrange’s Identity is shown. The identity focuses on the defect in Cauchy’s inequality.
The chapter then talks about the problem of writing a non negative polynomial as a sum of squares and derives interesting stuff. It is possible for a 1d case but is not possible for any 2d case.Minkowski’s Conjecture is discussed which basically states that any non negative polynomial in more than one variable cannot be written as the sum of squares of arbitrary polynomials. This conjecture was later proved by Hilbert
< On Geometry and Sums of Squares >
I loved this chapter as it was dealing with projection operators , householder reflectors and their connection with Cauchy-Schwartz Inequality. By illustrating a simple example of how our intuition fails sometimes when we move to higher dimensional world, the author reinforces the point that we need to have mathematical objects and properties well defined so that we can operate correctly in the slippery world of n dimensions. The chapter uses projection operator to prove Cauchy-Schwartz. It also derives a tighter bound for the product of two linear forms using projection and reflection operator. For a person well versed with linear algebra stuff, this chapter will be an absolute charm!. The takeaway from using these operators is that plain Euclidean geometry helps one to deepen the understanding of inequalities. The chapter ends with deriving Cauchy-Schwartz using a different geometric model, “Space-time geometry of Einstein and Minkowski.”. I found this part difficult to understand, despite clear exposition.
The highlight of this chapter is the connection between Gram-Schmidt and Inequalities. It turns out that one can use Gram-Schmidt to derive Cauchy-Schwartz inequality and a bunch of other inequalities like Bessel, Products of Linear Forms etc. If you use linear algebra at work, you are going to love this chapter.
< Consequences of Order >
Chebyshev’s Order Inequality and Chebyshev’s popular inequality relating to tail probabilities are covered in this chapter. I have skipped the Rearrangement inequality .. Will refer to it whenever I need it. As such, my naive mind could not relate it to any problem in the math-fin context .
< Convexity – The Third Pillar >
The chapter starts off by saying that
There are three great pillars of the theory of inequalities: positivity, monotonicity, and convexity. The notions of positivity and monotonicity are so intrinsic to the subject that they serve us steadily without ever calling attention to themselves, but convexity is different. Convexity expresses a second order effect.
What is Jensen’s Inequality ?
Jensen’s inequality is something which any math-fin student comes across in option valuation fundas, This chapter talks about Jensen’s inequality and a ton of inequalities which are variants of it. The basic approach is to crack a convex function , whose properties help you prove the desired inequality. So, you got to play around and check for various convex functions. However it is not all that difficult as it sounds. Basic functions like log, exponential , trigonometric most often than not, get you home.
< Integral Intermezzo >
Inequalities with integrand signs are explored. There are two themes or approaches which are explained using specific inequalities. The first one is to dissect the continuous form of integrand in to various intervals and prove the necessary inequality. The second approach is more involved and unique. It goes by the name “ Transform-Schwartz-Invert”, which basically means that any inequality needs to transformed( may be integration by parts), apply Cauchy Schwartz and then integrate by parts again to prove an inequality. The highlight of this chapter is proving the continuous form of Jensen’s inequality and showing the linkage between Jensen’s inequality and AM-GM inequality.
The continuous form of Jensen’s inequality shows up in a lot of areas in probability, where expectation of a random variable / moments of random variable are expressed in integral form. In all those places, Jensen’s continuous inequality is priceless. This chapter also shows an amazing connection between the fact that correlation and Centered version of Schwarz’s Inequality.
< The Ladder of Power Means >
The upper bound in Cauchy’s inequality can be generalized and this is precisely that is done in the chapter.
Generalized Power means are introduced and Power mean curve is explored . The curve shown below can be an effective tool for proving quite a number of inequalities, starting from the simple HM<=GM<=AM to intricate inequalities.
By having an understanding of generalized power means, you can identify power mean inequality lurking in a problem and exploit the properties.
< Holder’s Inequality >
From a math fin point of view, this chapter is probably the most important of all , as it talks about Holder’s and Minkowski’s inequality. Both these inequalities are very useful in dealing Lebesgue spaces. Holder’s inequality states that:
In the norm format, it is easier to recognize. Whenever Holder’s inequality is used, there is inevitable talk of Minskowski’s inequality. It arises mainly to prove that p-norm used in the Holder’s inequality makes sense. The triangular inequality that is need to justify p-norm is translated to Minkowski’s inequality.
The beauty of this book is that it shows connections between various inequalities. Triangle implies Cauchy, Minkowski implies Holders, Schwartz inner product version implies Cauchy, etc. After sometimes you start to wonder that , may be , you can solve a problem from any set of inequalities and it an ART to choose the right inequality and the right equality (sometimes) for the given context. No wonder the book is subtitled, “ The art of mathematical inequalities”.
The last part of the book covers Hilbert’s Inequality, Hardy’s Inequality , Symmetric Sums and Schur Convexity. I found the book as one of those books that is overwhelming in the first read. Not at all in the negative connotation. Your mind is bombarded with so many wonderful ways of looking at inequalities that you have to PAUSE and actually take stock of the ways to look at different inequalities. For example an inequality like,
can actually be solved by 1) Cauchy’s, 2) Combination of Power mean and Chebyshev’s Order Inequality , 3) Combination of Jensen’s and Holder’s Inequality 4) etc …Basically , this book will change the way you think about inequalities. You no longer stare at an inequality wondering what the hell it is supposed to imply , but you get in to pattern oriented thinking. You start making connections between inequalities and real life stuff that you come across , like a centered version of Schwarz’s inequality has a direct connection with the concept of correlation, Chebyshev’s Inequality with tail probabilities, Holder’s Inequality with norms in Lebesgue measurable space etc.
I am hoping to use this as a reference to inequalities as and when required. But more than a reference, I would definitely be going over this book a couple of more times in my working life as it equips you to connect seemingly unrelated stuff. Also the inequality tricks mentioned in the book are priceless.
Inequality, from a very practical point of view. gives you a handle on the bounds of a problem. Most of the problems in the math fin area are assumed to be cracked if you are able to form effective bounds, be it option pricing / hedge ratio / No arb bounds on implied vols,etc.
This book will give you a working knowledge of inequalities by providing a thorough explanation of 4 core principles, i.e Cauchy-Schwartz Inequality , AM-GM Inequality , Jensen’s Inequality and Holder’s inequality. Once you go think through these 4 principles and understand the tricks behind extending them, you will start noticing their variants in umpteen number of situations.
Prof.Trefethen David Bau
Prof Lloyd N.Trefethen and his student, David Bau have neatly organized all aspects of numerical linear algebra in to a set of 40 lectures. Each lecture revolves around one specific idea. I will try to summarize the key points of these lectures.
Every person who has solved any real life math fin problem knows that the classic numerical algebra is dead. Iterative methods are almost always used for finite difference methods, solving pde’s, optimization. Books such as these , which explain things clearly and give the intuition + math behind iterative methods should be considered as a treasure. To appreciate the iterative methods, it is important to know the classic algos. In that sense 31 lectures out of 40 lectures in the book revolve around the classic way of solving stuff. If you have the patience to slog through the 31 lectures, then you will definitely see the iterative methods in a different perspective altogether.
Lecture 1 & 2 : Matrix-Vector Multiplication , Orthogonal Vectors and Matrices
The first two lectures starts off with defining matrix multiplication, Null Space, Rank, Inner Product, Outer product , Unitary Matrices etc. Just enough concepts to get going instead of swamping the reader with Gaussian elimination /pivoting etc which is the way books on Linear Algebra typically begin.
Lecture 3: Norms
Norms assume an important role in numerical linear algebra as they serve as a measure of size and distance in a vector space. One generally comes across Vector Norms in lots of places but Matrix Norms are little special as one can define them based on the norms induced by the vector subspace OR norms independent of the vector subspace, which are called General Matrix Norms. The only norm that I had known before reading this book was inner product induced norms. Not many books discuss norms at a detailed level as this book does, and more so , right at the beginning of the book. Bounds for the norm of product of two matrices are derived in this note and are very helpful in deciding the backward stability of the algos.
Lecture 4: The Singular Value Decomposition
This note introduces the basic notation of SVD. SVD which was used relatively sparsely by scientists and engineers a few decades ago, has become one of the pivotal algos in Linear algebra. SVD is motivated by a nice geometric fact – “ SVD is the image of the unit sphere under any m*n matrix is a hyper ellipse”. By using SVD you get to play a Surgeon’s role , meaning you get to see all the internals of the matrix – the four subspaces of the matrix , Row space, Column Space, Null Space, Left Null Space. There are two versions of SVD, the Reduced SVD and the Full SVD. This note discusses both these versions and the differences between them. The other fascinating aspect of SVD is that every matrix has an SVD representation. This is akin to “ Every Square matrix having a Schur Decomposition”
Lecture 5: More on the SVD
Considering that SVD is used in tons of applications, it is not surprising that there is an extra chapter on SVD. This starts off by saying that SVD helps in choosing a smart basis in such a way that the new basis reduces Ax = b to a diagonal matrix relationship.
There are three differences between SVD and EVD( Eigen value decomposition)
SVD gives out orthogonal basis whereas EVD need not always(except for Normal operators)
SVD works with 2 basis whereas EVD works with one
SVD exists for any matrix whereas EVD works for square matrices only
The other appealing properties of SVD are
With SVD , you can have two different basis for the matrix of transformation.
With respect to the two orthogonal basis, the matrix of the transformation is a diagonal matrix
Inverse of a matrix can easily be found by SVD
One can do an SVD on the matrix and get the maximum singular value to be the norm of the matrix where norm is induced by a norm 2 vector space
In fact most of the important theorems in linear algebra can be easily derived using SVD.
Lecture 6: Projectors
It can safely be said that Projection operator is possibly THE MOST important operator of linear algebra. Most of the statistics based problems need a projection operator. Therefore this note becomes important as all the properties of Projectors are summarized. Some of them are
Range( I - P ) = null( P ) where I is identity operator
Null( I – P ) = Range( P )
Projector operator is idempotent
If Range( P ) = S1 and Range( P ) = S2, then P is the projector onto S1 along S2.
An Orthogonal Projector is one that projects on to a subspace S1 along S2 , where S1 and S2 are orthogonal.
Lecture 7: QR Factorization
This lecture introduces QR factorization because it connects almost all the algos in numerical linear algebra. Sadly this is never taught at the beginning of the linear algebra courses . Existence and Uniqueness of QR factorization is proved and its usage in solving Ax = b types problem is shown. When you step back from the world of matrices and then think about QR factorization in general, it strikes that you can use this in any application to make it computationally efficient. If you want to project a function in the vector space of polynomials, you can very well do that by starting with any basis and using QR to represent the problem in orthogonal basis.
Lecture 8: Gram-Schmidt Orthogonalization
Gram-Schmidt Orthogonalization is a method of factorization that can be termed as “triangular orthogonalization”, meaning a set of triangular matrices are applied on the matrix to produce an orthogonal matrix. Classic Gram-Schmidt is numerically unstable and hence this lecture introduces modified Gram-Schmidt which is nifty trick on the original algo. Unless you go over the two algorithms very carefully, you might not notice the difference between classical and modified algo. The operation count for Gram Schmidt is about ~ 2mn^2 for a m by n matrix. This note introduces a geometrical way of computing the operation count , an idea which is very interesting.
Lecture 9: MATLAB
Three examples are provided to motivate the reader to use Matlab for visualizing matrix operations. Well , You can use any stats /math based software. The authors main intention is that linear algebra stuff is understood only when you play with matrices.
First example deals with orthogonalization of Vandermonde Matrix which gives rise to Legendre polynomials. Why are Legendre polynomials used ? Well if you can represent a function as a linear combination of Legendre polynomials, you have the advantage that components are orthogonal. This makes a lot of computations easier.
Second example is a wonderful way to illustrate machine epsilon using QR decomposition and Gram Schmidt method. It is a superb example that shows the difference between between classical Gram Schmidt algo and Modified Gram Schmidt algo. The visual that is presented with this example is something that any reader will never forget.
The last example shows the instability of different algos when a 2*2 matrix has a rank close to 1. This basically hints at the problem of ill-conditioning. Another interesting exercise mentioned in this chapter is the use of SVD for image compression. Take an image and you can use SVD to find just enough rank 1 matrices to represent the sparse data. It can be best understood if you actually create an image and apply svd to compress the data.
Lecture 10: Householder Triangularization
This note introduces Household reflector method as an improvement over QR decomposition. QR decomposition using triangularization can be improved using Householder Orthogonal triangularization If there is m*n matrix, the ops count for improved gramschmidt is 2m n^2 whereas for Householder triangularization is 2m n^2 – (2/3) n^3 .Almost all the sparse matrices use Household triangularization for decreasing the ops count
Lecture 11: Least Squares Problems
This lecture introduces the three standard methods to solve Ax = b.
Linear equations – flip side : numerically unstable
QR decomposition to tide over numerical issues – flip side : bad results with rank deficient matrices
SVD works well with rank deficient matrices.flip side – yet to come across !
Lecture 12: Conditioning and Condition Numbers
This note introduces the concepts of condition number. Conditioning pertains to the perturbation behaviour of a mathematical problem. Stability pertains to the perturbation behaviour of an algorithm used to solve that problem on the computer.One can work with Absolute condition number / relative condition number. Both absolute and relative condition numbers have their uses, but the latter are more important in numerical analysis. This is ultimately because the floating point arithmetic used by computers introduces relative errors rather than absolute ones;
The note then gives examples of ill-conditioned problems. Polynomial root-finding is an example of ill-conditioned problem and hence finding eigen values from the characteristic equation needs to be avoided.
The condition for Matrix-Vector multiplication is then derived . The note ends with a very important theorem that talks about the condition number for Ax=b. The essence of this theorem is that one can at least give bounds to the condition number of the problem. The bound which is the norm of ( A )times norm of (A inv) has a special name , “Condition Number of the matrix”. This is relevant to the matrix and is not to be confused with the condition number of a problem.
Lecture 13: Floating Point Arithmetic
If you have never thought about machine representation of numbers , then this note tells you all. Floating point arithmetic basically deals with the way in which computer stores numbers. Any computer programmer/ algo developer / modeller must know about this floating point arithmetic. There are very subtle issues that need to be understood. In any case, this note only deals with machine epsilon. If you are comfortable with Big O notation,IEEE standards for number representation, you can safely skip this note.
Lecture 14: Stability
This note introduces the concepts of accuracy, stability and backward stability. A stable algorithm is one which gives nearly the right answer to nearly right question. A backward stable algorithm is one that gives the right answer to nearly right question. An important theorem mentioned in this lecture is that
For problems f and algorithms f’ defined on finite-dimensional spaces X and Y, the properties of accuracy, stability, and backward stability all hold or fail to hold independently of the choice of norms in X and Y.
The above theorem allows you to use any norm for checking the stability and accuracy aspects.
Lecture 15: More on Stability
This note talks about stability and condition number of the matrix. It shows that solving eigen values using characteristic equation is a bad idea as the polynomial resulting from the characteristic equation is usually ill-conditioned, meaning whatever method you use to extract the roots of the polynomial, they are going to be extremely sensitive to minor perturbations in the polynomial coefficients.
Given a problem, it is obvious that conditionality is going to affect the accuracy of the solution.Hence if one has to choose an algo or evaluate an algo, it is backward stability that matters. The condition number of A is so global a property as to be more or less invisible at the level of the individual floating point operations involved in solving Ax = b.
The basic takeaway of this note is that algo needs to be evaluated from backward stability perspective and the solution needs to be evaluated based on conditionality and backward stability perspective.
Lecture 16: Stability of Householder Triangularization
This note introduces the concept of stability using an example of an ill-conditioned matrix. If you generate a random triangular matrix, the column spaces of the matrix are going to be exceedingly ill-conditioned as a function of the entries of the matrix. This means that whatever algo you use for QR decomposition, the individual Q and R are going to show large absolute and relative errors. However the astonishing thing is that Householder gives the product of QR in such a way that QR differs from the original matrix by a very small value which is of the order (machine epsilon). This example in the note makes you realize that algo needs to be judged from backward stability . WHY? Well, the problem could be ill conditioned and hence blaming the algo for errors is not the right approach.
To reiterate, the most important aspect in linear algebra is backward stability, meaning, giving a right answer to an approximately right question. Householder Triangularization fits this requirement and hence qualifies as a backward stable algorithm. The QR thus obtained from Housholder Triangularization would be the right factorization for a perturbed A instead of A. It is stated in the note that Householder triangularization is backward stable for all matrices.
QR factorization is not an end in itself but is a means to solve the MEGA equation of linear algebra Ax = b. If the Householder factorization QR is backward stable, is it enough to make it a satisfactory piece of larger algorithm? Thankfully the answer is YES and hence the algo for calculating x in Ax = b using Householder triangularization is ALSO backward stable. Basically the point that this note makes is that , if you can solve x using an algo for an approximate A, i.e A + delta(A) where delta(A) depends on machine epsilon, then the algo is good enough to be used in various applications.
This note also mentions about SVD where one can apply SVD on an ill conditioned matrix and get a lesser forward error as compared to House Holder factorization. Though SVD does not reduce the forward error to 0 as the other component effecting the result is the conditionality of the matrix. A extremely important thing to remember, i.e conditionality and algo both effect the results. Algo can be evaluated based on backward stability.
Lecture 17: Stability of Back Substitution
This note systematically deals with all the aspects of a back substitution algorithm in proving that it is a backward stable algorithm. For a simple back substitution algo for Rx = b, it is shown that a small perturbation in R gives an exact answer for x, i.e (R+ delta(R)) x’ = b where x’ denotes the floating point representation of x.
The above bounds are derived for the R matrix. Basically we are seeing a component wise bounds rather than a weaker norm based bounds in the above proof. In the early decades of numerical analysis after the Second World War, most error estimates were obtained in the norm-wise form, whereas in more recent years there has been a shift toward component-wise analysis, since such results are sharper and algorithms that satisfy component-wise bounds are less sensitive to scaling of variables.
Lecture 18: Conditioning of Least Squares Problems
This note talks about the effect of perturbation of A and b on x and y. The following matrix summarizes the results in terms of three dimensionless parameters κ , θ , η. Kappa represents the conditioning number, theta gives an idea of the closeness of fit, and eta represents how much the projection falls short of condition number.
It is imperative to calculate these 4 numbers as soon as you see as Ax =b least squares problem before using an algo. The numbers in the matrix will give an intuitive idea about the nature of problem.
Lecture 19: Stability of Least Squares Algorithms
This note checks various algos solving Ax = b, such as Householder Triangularization, Cholesky, Modified Gram Schmidt, SVD for stability and accuracy issues. The results from various algos gives the reader an idea of relative strengths and limitations of the decomposition algorithms. The input matrix is deliberately chosen as a rank deficient Vandermonde matrix to show the performance of various algos. After going through this note, you will always tend to use SVD for any factorization for rank deficient matrices, as it is likely to be a superior over other algos. The highlight of this chapter is that it will make the reader realize that , Whenever one comes across error estimate, one needs to always gauge the individual contribution of ill-conditioning AND contribution of algo-performance. There is also a nice reasoning given for the instability of cholesky decomposition algo used to solve Ax = b.
Lecture 20: Gaussian Elimination
This note starts off by stating the various factorization in words
Gram-Schmidt : A = QR by Triangular Orthogonalization
Householder : A = QR by Orthogonal Triangularization
Gaussian Elimination : A = LU by Triangular Triangularization
The Gaussian elimination is one of the basic factorizations used for solving a system of equations. Basically it involves solving Ax = b by factorizing A as LU and then using forward and backward substitutions. The problem with plain vanilla LU is that it is not backward stable. Operation count is about (2m^3)/3 flops for a m by n matrix.
Lecture 21: Pivoting
This note introduces complete pivoting / partial pivoting to improve the performance of Gaussian elimination. Basically this involves factorizing PA = LU or PAQ = LU , where P and Q are row and column permutation matrices
Lecture 22: Stability of Gaussian Elimination
This note goes in to the details of the reasons behind the instability of Gaussian elimination. The note says “We shall not attempt to give a full explanation of why the matrices for which Gaussian elimination is unstable are so rare. This would not be possible, as the matter is not yet fully understood. But we shall present an outline of an explanation.” The note concludes with a positive tone saying that Gaussian elimination with partial pivoting algo is highly unstable for certain matrices of class A. For instability to occur, however, the column spaces of A must be skewed in a very special fashion, one that is exponentially rare in at least one class of random matrices. Decades of computational experience suggest that matrices whose column spaces are skewed in this fashion arise very rarely in applications. Basically you can still rely on the partial pivoting algo for most of real world applications.
Lecture 23 :Cholesky Factorization
This note talks about a matrix decomposition applied to Hermitian positive definite matrices. The algo is a variant of Gaussian elimination which exploits the symmetry present in the matrix. The operation count is (m^3 ) / 3 flops. All of the subtleties of the stability analysis of Gaussian elimination vanish for Cholesky factorization. This algorithm is always stable. Intuitively, the reason is that the factors of R can never grow large. The stability of Cholesky factorization is achieved without the need for any pivoting. Intuitively, one may observe that this is related to the fact that most of the weight of a hermitian positive definite matrix is on the diagonal.
Lecture 24: Eigenvalue Problems
The note starts off by saying that eigen value problems are particularly interesting as the algos for solving them are powerful, yet not obvious. Basic definitions of eigen value and eigen vectors are provided at the beginning of the note.Eigenvalue and Eigen vectors are useful for two reasons, one algorithmic, the other physical. Algorithmically, eigen value analysis can simplify solutions of certain problems by reducing a coupled system to a collection of scalar problems. Physically , eigen value analysis can give insight in to the behaviour of evolving systems governed by linear equations.
Usage of EVD produces the following form X need not be orthogonal for a general eigen value decomposition. However it so happens that one can choose X in such a way that they are orthogonal in which case, the diagonalization is termed as Unitary Diagonalization. Only Normal matrices are unitarily diagonalizable. This means that eigen vector decomposition and svd coincide only for Normal matrices( transformations for which T*T = TT*)
The characteristic polynomial is then defined and is used to define algebraic multiplicity.The concept of algebraic multiplicity and geometric multiplicity is introduced so as to classify matrices as defective matrices. A defective matrix is one where algebraic multiplicity of one or more eigen values is greater than the respective geometric multiplicity. What are these multiplicities? Algebraic multiplicity of an eigen value is its multiplicity as a root of the characteristic equation. Geometric multiplicity of a eigen value lambda is equal to the dimension of (A-lambda* I) ^n for a n by n matrix. The note concludes by introducing Schur Factorization, which is applicable to any square matrix.
The major takeaway from this note is as follows :
Lecture 25: Overview of Eigenvalue Algorithms
The next five lectures talk about various aspects of eigen value algorithms. I was very sceptical about going over these lectures as it was going in to a depth which was probably not required for my work. I mean eigen(A) gives all the results that you need. Why should I care about the internal workings about EVD algos ? Harbouring such feelings, I decided to speed read and see whether there is any thing that I could get from the dense lectures about eigen value decomposition.
The overview note was brilliant to say the least. It makes the brilliant connection between a polynomial equation and an EVD problem and puts forth a beautiful argument that since there is no formula that exists for expressing the roots of an arbitrary polynomial, given its coefficients, there is no closed form solution/no finite number of steps that can be programmed to find the eigen values. This means that eigen value solver must be iterative.
The goal of eigen vector solver is to produce sequences of numbers that converge rapidly towards eigen values. In this respect eigen value computations are more representative of scientific computing than solutions of linear equations. There are two phases for any eigen solver. Phase1 is to reduce the input matrix in to an upper Hessenberg matrix and in the second phase, an iteration is applied to generate a formally infinite sequence of Hessenberg matrices that converge to Triangular form. Once Triangular matrix is formed, Similar matrices argument is used to show that eigen values of the initial matrix and the triangular matrix are the same.
Lecture 26: Reduction to Hessenberg or Tridiagonal Form
This note shows , why is it a bad idea to use householder type matrices for reducing the input matrix to a upper Hessenberg matrix. Subsequently it goes in to developing the algo that actually reduces the input matrix in to upper triangular Hessenberg matrix. The next 4 lectures goes in to great depths to explain the inner workings of an EVD algo. The last lecture in this note talks about the implementation of SVD Algo.
The last part of the book deals with iterative methods which are extremely useful in solving real world problems. In the recent times, they have overshadowed the classic methods of linear algebra. In one sense, if you are going to use computers to solve something, most often than not, you will have to resort to iterative methods. I will try to post the main points of the last part of the book, “Iterative methods” , some other day.