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I found these images on twitter via (Paige Bailey) @DynamicWebPaige  Found them so hilarious that I thought they deserved a blog post (Needless to say, click on each image for a higher resolution version).

PS: A quick search indicates that these are from “Land of Lisp: Learn to Program in List, One Game at a time” by M. D. Conrad Barski.

The 40s and 50s

40s60s and 70s

60s

80s and 90s

80s

2000

90s

Recently I was going through Shannon’s original 1948 Information Theory paper and a paper by Kolmogorov from 1983 that places the differences between “Shannon Information” and “Algorithmic Information” in sharp relief. After much information diffusion over the past decades the difference is quite obvious and particularly interesting to contrast nonetheless. Nevertheless I found these two paragraphs from these two papers interesting, if for nothing then for historical reasons.

“The fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point. Frequently the messages have meaning; that is they refer to or are correlated according to some system with certain physical or conceptual entities. These semantic aspects of communication are irrelevant to the engineering problem. The significant aspect is that the actual message is one selected from a set of possible messages. The system must be designed to operate for each possible selection, not just the one which will actually be chosen since this is unknown at the time of design.” (C. E. Shannon, A Mathematical Theory of Communication, 1948).
“Our definition of the quantity of information has the advantage that it refers to individual objects and not to objects treated as members of a set of objects with a probability distribution given on it. The probabilistic definition can be convincingly applied to the information contained, for example, in a stream of congratulatory telegrams. But it would not be clear how to apply it, for example, to an estimate of the quantity of information contained in a novel or in the translation of a novel into another language relative to the original. I think that the new definition is capable of introducing in similar applications of the theory at least clarity of principle.” (A. N. Kolmogorov, Combinatorial Foundations of Information Theory and the Calculus of Probabilities, 1983).
Note the reference that Shannon makes is to a message selected from a set of possible messages (and hence the use of probability theory), his problem was mainly concerned with the engineering application of communication. While Kolmogorov talks of individual objects and is not directly concerned with communication and hence the usage of computability (the two notions are related ofcourse, for example the expected Kolmogorov Complexity is equal to Shannon entropy).

The No-Free-Lunch theorems (Wolpert and Macready) are one of the mainstays in Machine Learning and optimization. Crudely, the NFL says that “for every learner there exists a distribution in which it fails”, giving a precise handle to reason about why a given learning algorithm might fail on certain tasks (or, “specialization” of a learning algorithm might just be accidental). It gives a good justification why it is useful to incorporate prior informaion about the problem at hand to perform better. Oftentimes the NFL is used to argue that a general purpose learning algorithm is theoretically impossible. This is incorrect. Indeed, universal learning algorithms exist and there is a (possibly very small) free lunch, at least for the case of “interesting problems” (thanks to Vick for pointing out these papers).

The view “for every learner there exists a distribution in which it fails” is very useful. However I had recently been thinking that is particularly instructive to think of No Free Lunch as a kind of diagnolization argument (this is just a different way of thinking about the prior statement). I am not completely sure if this works but it sounds plausible and makes me see NFL in a different light. It might also be that there is some subtle absrudity or flaw in this kind of thinking. However, I think the NFL theorem can be seen as a simple corollary of the following incompleteness theorem in Kolmogorov Complexity.

Theorem (Chaitin): There exists a constant L (which only depends on the particular axiomatic system and the choice of description language) such that there does not exist a string s for which the statement the “The Kolmogorov Complexity of s is greater than L” (as formalized in S) can be proven within the axiomatic system S. 

Stated informally: “There’s a number L such that we can’t prove the Kolmogorov complexity of any specific string of bits is more than L”. In particular, you can think of any learning machine as a compression scheme (since compression implies learning, we can think of any learning algorithm as a way to compress the data) of some fixed Kolmogorov Complexity (consider the most optimal program for that machine, the programming language is irrelevant as long as it is Turing Complete. Consider the description length of it in bits. That is its Kolmogorov Complexity). Now for any such learning machine (or class of learning machines w.l.o.g) you can construct a data string which has Kolmogorov Complexity greater than what this learning machine has. i.e. this data string is random from the point of view of this learning machine as it is incompressible for it. In short IF the data string has some Kolmogorov Complexity K and your learning machine has Kolmogorov Complexity k and k < K then you will never be able to find any structure in your data string i.e. can never show that it has a KC greater than k. Thus the data string given to our learner would appear to be structureless. Thus no free lunch is just a simple diagonalization argument in the spirit of incompleteness results. One can always construct a string that is Kolmogorov Random or structureless w.r.t our encoding scheme. Ofcourse the Kolmogorov Complexity is uncomputable. However I think the above argument works even when one is talking of upper bounds on the actual Kolmogorov Complexity, but I am not very sure about it or if there are any problems with it.

Also, if one assumes that that there is no reason to believe that structureless functions exist (if we assume that the data is generated by a computational process then there is probably no reason to believe that it is structureless i.e. the problem is “interesting”). And if this is the case, then you would always have a machine in your library of learning algorithms that will be able to find some upper bound on the Kolmogorov Complexity of the data string (much less than the machine itself). This search might be done using Levin’s universal search, for example. I think this line of reasoning leads naturally, to thinking why NFL doesn’t say anything about whether universal learning algorithms can exist.

PS: This thought train about NFL as diagonalization was inspired by a post by David McAllester on what he calls the “Free Lunch Theorem” over here. Some of this appeared as a comment there. However, now I have been inspired enough to study some of the papers concerned including his. (Unrelated to all this. I particularly found likening Chomsky’s Universal Grammar as simply an invocation of NFL on information theoretic grounds in his post very enlightening).

Over the past 4-5 months whenever there is some time to spare, I have been working through The Cauchy-Schwarz Master Class by J. Michael Steele. And, although I am still left with the last two chapters, I have been reflecting on the material already covered in order to get a better perspective on what I have been slowly learning over the months. This blog post is a small exercise in this direction.

Ofcourse, there is nothing mysterious about proving the Cauchy-Schwarz inequality; it is fairly obvious and basic. But I thought it still might be instructive (mostly to myself) to reproduce some proofs that I know out of memory (following a maxim of my supervisor on a white/blackboard blogpost). Although, why Cauchy-Schwarz keeps appearing all the time and what makes it so useful and fundamental is indeed quite interesting and non-obvious. And like Gil Kalai notes, it is also unclear why is it that it is Cauchy-Schwarz which is mainly useful. I feel that Steele’s book has made me appreciate this importance somewhat more (compared to 4-5 months ago) by drawing to many concepts that link back to Cauchy-Schwarz.

Before getting to the post, a word on the book: This book is perhaps amongst the best mathematics book that I have seen in many years. True to its name, it is indeed a Master Class and also truly addictive. I could not put aside the book completely once I had picked it up and eventually decided to go all the way. Like most great books, the way it is organized makes it “very natural” to rediscover many susbtantial results (some of them named) appearing much later by yourself, provided you happen to just ask the right questions. The emphasis on problem solving makes sure you make very good friends with some of the most interesting inequalities. The number of inequalities featured is also extensive. It starts off with the inequalities dealing with “natural” notions such as monotonicity and positivity and later moves onto somewhat less natural notions such as convexity. I can’t recommend this book enough!

Now getting to the proofs: Some of these proofs appear in Steele’s book, mostly as either challenge problems or as exercises. All of them were solvable after some hints.

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Proof 1: A Self-Generalizing proof

This proof is essentially due to Titu Andreescu and Bogdan Enescu and has now grown to be my favourite Cauchy-Schwarz proof.

We start with the trivial identity (for a, b, x, y \in \mathbb{R} ):

Identity 1: (ay - bx)^2 \geq 0

Expanding we have

a^2y^2 + b^2x^2 - 2abxy \geq 0

Rearranging this we get:

\displaystyle \frac{a^2y}{x} + \frac{b^2x}{y} \geq 2ab

Further: \displaystyle a^2 + b^2 + \frac{a^2y}{x} + \frac{b^2x}{y} \geq (a+b)^2;

Rearranging this we get the following trivial Lemma:

Lemma 1: \displaystyle \frac{(a+b)^2}{(x+y)} \leq \frac{a^2}{x} + \frac{b^2}{y}

Notice that this Lemma is self generalizing in the following sense. Suppose we replace b with b + c and y with y + z, then we have:

\displaystyle \frac{(a+b+c)^2}{(x+y+z)} \leq \frac{a^2}{x} + \frac{(b+c)^2}{y+z}

But we can apply Lemma 1 to the second term of the right hand side one more time. So we would get the following inequality:

\displaystyle \frac{(a+b+c)^2}{(x+y+z)} \leq \frac{a^2}{x} + \frac{b^2}{y} + \frac{c^2}{z}

Using the same principle n times we get the following:

\displaystyle \frac{(a_1+a_2+ \dots a_n)^2}{(x_1+x_2+ \dots + x_n)} \leq \frac{a_1^2}{x_1} + \frac{a_2^2}{x_2} + \dots + \frac{a_n^2}{x_n}

Now substitute a_i = \alpha_i \beta_i and x_i = \beta_i^2 to get:

\displaystyle ( \sum_{i=1}^n \alpha_i \beta_i )^2 \leq \sum_{i=1}^n ( \alpha_i )^2 \sum_{i=1}^n ( \beta_i )^2

This is just the Cauchy-Schwarz Inequality, thus completing the proof.

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Proof 2: By Induction

Again, the Cauchy-Schwarz Inequality is the following: for a, b \in \mathbb{R}

\displaystyle ( \sum_{i=1}^n a_i b_i )^2 \leq \sum_{i=1}^n ( a_i )^2 \sum_{i=1}^n ( b_i )^2

For proof of the inequality by induction, the most important thing is starting with the right base case. Clearly n = 1 is trivially true, suggesting that it is perhaps not of much import. So we consider the case for n = 2. Which is:

\displaystyle ( a_1 b_1 + a_2 b_2 )^2 \leq ( a_1^2 + a_2^2 ) (b_1^2 + b_2^2)

To prove the base case, we simply expand the expressions. To get:

\displaystyle a_1^2 b_1^2 + a_2^2 b_2^2 + 2 a_1 b_1 a_2 b_2 \leq a_1^2 b_1^2 + a_1^2 b_2^2 + a_2^2 b_1^2 + a_2^2 b_2^2

Which is just:

\displaystyle a_1^2 b_2^2 + a_2^2 b_1^2 - 2 a_1 b_1 a_2 b_2 \geq 0

Or:

\displaystyle (a_1 b_2 - a_2 b_1 )^2 \geq 0

Which proves the base case.

Moving ahead, we assume the following inequality to be true:

\displaystyle ( \sum_{i=1}^k a_i b_i )^2 \leq \sum_{i=1}^k ( a_i )^2 \sum_{i=1}^k ( b_i )^2

To establish Cauchy-Schwarz, we have to demonstrate, assuming the above, that

\displaystyle ( \sum_{i=1}^{k+1} a_i b_i )^2 \leq \sum_{i=1}^{k+1} ( a_i )^2 \sum_{i=1}^{k+1} ( b_i )^2

So, we start from H(k):

\displaystyle ( \sum_{i=1}^k a_i b_i )^2 \leq \sum_{i=1}^k ( a_i )^2 \sum_{i=1}^k ( b_i )^2

we further have,

\displaystyle \Big(\sum_{i=1}^k a_i b_i\Big) + a_{k+1}b_{k+1} \leq \Big(\sum_{i=1}^k ( a_i )^2\Big)^{1/2} \Big(\sum_{i=1}^k ( b_i )^2\Big)^{1/2} + a_{k+1}b_{k+1} \ \ \ \ (1)

Now, we can apply the case for n = 2. Recall that: \displaystyle a_1 b_1 + a_2 b_2 \leq (a_1^2 + a_2^2)^{1/2} (b_1^2 + b_2^2)^{1/2}

Thus, using this in the R. H. S of (1) , we would now have:

\displaystyle \Big(\sum_{i=1}^{k+1} a_i b_i\Big) \leq \Big(\sum_{i=1}^k ( a_i )^2 + a_{k+1}^2\Big)^{1/2} \Big(\sum_{i=1}^k ( b_i )^2 + b_{k+1}^2\Big)^{1/2}

Or,

\displaystyle \Big(\sum_{i=1}^{k+1} a_i b_i\Big) \leq \Big(\sum_{i=1}^{k+1} ( a_i )^2 \Big)^{1/2} \Big(\sum_{i=1}^{k+1} ( b_i )^2 \Big)^{1/2}

This proves the case H(k+1) on assuming H(k). Thus also proving the Cauchy-Schwarz inequality by the principle of mathematical induction.

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Proof 3: For Infinite Sequences using the Normalization Trick:

We start with the following question.

Problem: For a, b \in \mathbb{R} If \displaystyle \Big(\sum_{i=1}^{\infty} a_i^2 \Big) < \infty and \displaystyle \Big(\sum_{i=1}^{\infty} b_i^2 \Big) < \infty then is \displaystyle \Big(\sum_{i=1}^{\infty} |a_i||b_i|\Big) < \infty ?

Note that this is easy to establish. We simply start with the trivial identity (x-y)^2 \geq 0 which in turn gives us \displaystyle xy \leq \frac{x^2}{2} + \frac{y^2}{2}

Next, take x = |a_i| and y = |b_i| on summing up to infinity on both sides, we get the following:

\displaystyle \Big( \sum_{i=1}^{\infty} |a_i||b_i| \Big)^2 \leq \frac{1}{2}\Big(\sum_{i=1}^{\infty} a_i^2\Big) + \frac{1}{2} \Big(\sum_{i=1}^{\infty} b_i^2\Big)\ \ \ \ \ \ \ \ (2)

From this it immediately follows that

\displaystyle \Big(\sum_{i=1}^{\infty} |a_i||b_i|\Big) < \infty

Now let

\displaystyle \hat{a}_i = \frac{a_i}{\Big(\sum_j a_i^2\Big)^{1/2}} and

\displaystyle \hat{b}_i = \frac{b_i}{\Big(\sum_j b_i^2\Big)^{1/2}}; substituting in (2), we get:

\displaystyle \Big( \sum_{i=1}^{\infty} |\hat{a}_i||\hat{b}_i| \Big)^2 \leq \frac{1}{2}\Big(\sum_{i=1}^{\infty} \hat{a}_i^2\Big) + \frac{1}{2} \Big(\sum_{i=1}^{\infty} \hat{b}_i^2\Big) or,

\displaystyle \Bigg(\sum_{i = 1}^{\infty}\frac{a_i}{\Big(\sum_j a_j^2\Big)^{1/2}}\frac{b_i}{\Big(\sum_j b_j^2\Big)^{1/2}}\Bigg)^2 \leq \frac{1}{2} + \frac{1}{2}

Which simply gives back Cauchy’s inequality for infinite sequences thus completing the proof:

\displaystyle \Big(\sum_{i=1}^{\infty} a_i b_i\Big)^2 \leq \Big(\sum_{i=1}^{\infty}a_i^2\Big) \Big(\sum_{i=1}^{\infty}b_i^2\Big)

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Proof 4: Using Lagrange’s Identity

We first start with a polynomial which we denote by \mathbf{Q}_n:

\mathbf{Q}_n = \big(a_1^2 + a_2^2 + \dots + a_n^2 \big) \big(b_1^2 + b_2^2 + \dots + b_n^2 \big) - \big(a_1b_1 + a_2b_2 + \dots + a_nb_n\big)^2

The question to now ask, is \mathbf{Q}_n \geq 0? To answer this question, we start of by re-writing \mathbf{Q}_n in a “better” form.

\displaystyle \mathbf{Q}_n = \sum_{i=1}^{n}\sum_{j=1}^{n} a_i^2 b_j^2 - \sum_{i=1}^{n}\sum_{j=1}^{n} a_ib_i a_jb_j

Next, as J. Michael Steele puts, we pursue symmetry and rewrite the above so as to make it apparent.

\displaystyle \mathbf{Q}_n = \frac{1}{2} \sum_{i=1}^{n}\sum_{j=1}^{n} \big(a_i^2 b_j^2 + a_j^2 b_i^2\big) - \frac{2}{2}\sum_{i=1}^{n}\sum_{j=1}^{n} a_ib_i a_jb_j

Thus, we now have:

\displaystyle \mathbf{Q}_n = \frac{1}{2} \sum_{i=1}^{n}\sum_{j=1}^{n} \big(a_i b_j - a_j b_i\big)^2

This makes it clear that \mathbf{Q}_n can be written as a sum of squares and hence is always postive. Let us write out the above completely:

\displaystyle \sum_{i=1}^{n}\sum_{j=1}^{n} a_i^2 b_j^2 - \sum_{i=1}^{n}\sum_{j=1}^{n} a_ib_ib_jb_j = \frac{1}{2}\sum_{i=1}^{n}\sum_{j=1}^{n} \big(a_ib_j -a_jb_i\big)^2

Now, reversing the step we took at the onset to write the L.H.S better, we simply have:

\displaystyle \sum_{i}^{n} a_i^2 \sum_{}^{n} b_i^2 - \big(\sum_{i=1}^n a_ib_i\big)^2 = \frac{1}{2}\sum_{i=1}^{n}\sum_{j=1}^{n} \big(a_ib_j -a_jb_i\big)^2

This is called Lagrange’s Identity. Now since the R.H.S. is always greater than or equal to zero. We get the following inequality as a corrollary:

\displaystyle \big(\sum_{i=1}^n a_ib_i\big)^2 \leq \sum_{i}^{n} a_i^2 \sum_{}^{n} b_i^2

This is just the Cauchy-Schwarz inequality, completing the proof.

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Proof 5: Gram-Schmidt Process gives an automatic proof of Cauchy-Schwarz

First we quickly review the Gram-Schmidt Process: Given a set of linearly independent elements of a real or complex inner product space \big(V,\langle\cdot, \cdot\rangle\big), \mathbf{x_1}, \mathbf{x_2}, \dots, \mathbf{x_n}. We can get an orthonormal set of n elemets \mathbf{e_1}, \mathbf{e_2}, \dots, \mathbf{e_n} by the simple recursion (after setting \displaystyle \mathbf{e_1 = \frac{\mathbf{x_1}}{\|x_1 \|}}).

\displaystyle \mathbf{z_k} = \mathbf{x_k} - \sum_{j=1}^{k-1} \langle\mathbf{x_k},\mathbf{e_j}\rangle\mathbf{e_j} and then

\displaystyle \mathbf{e_k} = \frac{\mathbf{z_k}}{\|\mathbf{z_k}\|}

for k = 2, 3, \dots, n.

Keeping the above in mind, assume that \| x\| = 1. Now let x = e_1. Thus, we have:

\mathbf{z} = \mathbf{y} - \langle \mathbf{y}, \mathbf{e_1}\rangle\mathbf{e_1}

Giving: \displaystyle \mathbf{e_2} = \frac{\mathbf{z}}{\|\mathbf{z}\|}. Rearranging we have:

\displaystyle \|\mathbf{z}\|\mathbf{e_2} = \mathbf{y} - \langle \mathbf{y}, \mathbf{e_1}\rangle\mathbf{e_1} or

\displaystyle \mathbf{y} = \langle \mathbf{y},\mathbf{e_1}\rangle \mathbf{e_1} + \|\mathbf{z}\|\mathbf{e_2} or

\displaystyle \mathbf{y} = \mathbf{c_1} \mathbf{e_1} + \mathbf{c_2}\mathbf{e_2} where c_1, c_2 are constants.

Now note that: \displaystyle \langle\mathbf{x},\mathbf{y}\rangle = c_1 and

\displaystyle \langle \mathbf{y},\mathbf{y}\rangle = |c_1|^2 + |c_2|^2. The following bound is trivial:

\displaystyle |c_1| \leq (|c_1|^2 + |c_2|^2)^{1/2}. But note that this is simply \langle x,y \rangle \leq \langle y,y \rangle^{1/2}

Which is just the Cauchy-Schwarz inequality when \|x\| = 1.

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Proof 6: Proof of the Continuous version for d =2; Schwarz’s Proof

For this case, the inequality may be stated as:

Suppose we have S \subset \mathbb{R}^2 and that f: S \to \mathbb{R} and g: S \to \mathbb{R}. Then consider the double integrals:

\displaystyle A = \iint_S f^2 dx dy, \displaystyle B = \iint_S fg dx dy and \displaystyle C = \iint_S g^2 dx dy. These double integrals must satisfy the following inequality:

|B| \leq \sqrt{A} . \sqrt{C}.

The proof given by Schwarz as is reported in Steele’s book (and indeed in standard textbooks) is based on the following observation:

The real polynomial below is always non-negative:

\displaystyle p(t) = \iint_S \Big( t f(x,y) + g(x,y) \Big)^2 dx dy = At^2 + 2Bt + C

p(t) > 0 unless f and g are proportional. Thus from the binomial formula we have that B^2 \leq AC, moreover the inequality is strict unless f and g are proportional.

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Proof 7: Proof using the Projection formula

Problem: Consider any point x \neq 0 in \mathbb{R}^d. Now consider the line that passes through this point and origin. Let us call this line \mathcal{L} = \{ tx: t \in \mathbb{R}\}. Find the point on the line closest to any point v \in \mathbb{R}^d.

If P(v) is the point on the line that is closest to v, then it is given by the projection formula: \displaystyle P(v) = x \frac{\langle x, v \rangle }{\langle x, x \rangle}

This is fairly elementary to establish. To find the value of t, such that distance \rho(v,tx) is minimized, we can simply consider the squared distance \rho^2(v,tx) since it is easier to work with. Which by definition is:

\displaystyle \rho^2(v,tx) = \langle v - x, v - tx \rangle

which is simply:

\displaystyle \rho^2(v,tx) = \langle v, v \rangle - 2t \langle v, x \rangle + t^2 \langle x, x \rangle

\displaystyle = \langle x, x \rangle \bigg( t^2 -2t \frac{\langle v, x \rangle}{\langle x, x \rangle} + \frac{\langle v, v \rangle}{\langle x, x \rangle}\bigg)

\displaystyle = \langle x, x \rangle \bigg\{ \bigg(t - \frac{\langle v,x \rangle}{\langle x,x \rangle}\bigg)^2 - \frac{\langle v,x \rangle^2}{\langle x,x \rangle^2}\bigg\} + \frac{\langle v, v \rangle}{\langle x, x \rangle}\bigg)

\displaystyle = \langle x, x \rangle \bigg\{ \bigg(t - \frac{\langle v,x \rangle}{\langle x,x \rangle}\bigg)^2 - \frac{\langle v,x \rangle^2}{\langle x,x \rangle^2} + \frac{\langle v, v \rangle}{\langle x, x \rangle} \bigg\}

\displaystyle = \langle x, x \rangle \bigg\{ \bigg(t - \frac{\langle v,x \rangle}{\langle x,x \rangle}\bigg)^2 - \frac{\langle v,x \rangle^2}{\langle x,x \rangle^2} + \frac{\langle v, v \rangle \langle x, x \rangle}{\langle x, x \rangle^2} \bigg\}

So, the value of t for which the above is minimized is \displaystyle \frac{\langle v,x \rangle}{\langle x,x \rangle}. Note that this simply reproduces the projection formula.

Therefore, the minimum squared distance is given by the expression below:

\displaystyle \min_{t \in \mathbb{R}} \rho^2(v, tx) = \frac{\langle v,v\rangle \langle x,x \rangle - \langle v,x \rangle^2}{\langle x,x \rangle}

Note that the L. H. S is always positive. Therefore we have:

\displaystyle \frac{\langle v,v\rangle \langle x,x \rangle - \langle v,x \rangle^2}{\langle x,x \rangle} \geq 0

Rearranging, we have:

\displaystyle \langle v,x \rangle^2 \leq \langle v,v\rangle \langle x,x \rangle

Which is just Cauchy-Schwarz, thus proving the inequality.

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Proof 8: Proof using an identity

A variant of this proof is amongst the most common Cauchy-Schwarz proofs that are given in textbooks. Also, this is related to proof (6) above. However, it still has some value in its own right. While also giving an useful expression for the “defect” for Cauchy-Schwarz like the Lagrange Identity above.

We start with the following polynomial:

P(t) = \langle v - tw, v - tw \rangle. Clearly P(t) \geq 0.

To find the minimum of this polynomial we find its derivative w.r.t t and setting to zero:

P'(t) = 2t \langle w, w \rangle - 2 \langle v, w \rangle = 0 giving:

\displaystyle t_0 = \frac{\langle v, w \rangle}{\langle w, w \rangle}

Clearly we have P(t) \geq P(t_0) \geq 0. We consider:

P(t_0) \geq 0, substituting \displaystyle t_0 = \frac{\langle v, w \rangle}{\langle w, w \rangle} we have:

\displaystyle \langle v,v \rangle - \frac{\langle v, w \rangle}{\langle w, w \rangle} \langle v,w \rangle - \frac{\langle v, w \rangle}{\langle w, w \rangle} \langle w,v \rangle + \frac{\langle v, w \rangle^2}{\langle w, w \rangle^2}\langle w,w \rangle \geq 0 \ \ \ \ \ \ \ \ (A)

Just rearrangine and simplifying:

\displaystyle \langle v,v \rangle \langle w,w \rangle - \langle v, w \rangle^2 \geq 0

This proves Cauchy-Schwarz inequality.

Now suppose we are interested in an expression for the defect in Cauchy-Schwarz i.e. the difference \displaystyle \langle v,v \rangle \langle w,w \rangle - \langle v, w \rangle^2. For this we can just consider the L.H.S of equation (A) since it is just \displaystyle \langle w,w \rangle \Big(\langle v,v \rangle \langle w,w \rangle - \langle v, w \rangle^2\Big).

i.e. Defect =

\displaystyle \langle w,w \rangle \bigg(\langle v,v \rangle - 2 \frac{\langle v,w \rangle^2}{\langle w,w \rangle} + \frac{\langle v,w \rangle^2}{\langle w,w \rangle}\bigg)

Which is just:

\displaystyle \langle w,w \rangle\bigg(\Big\langle v - \frac{\langle w,v \rangle}{\langle w,w \rangle}w,v - \frac{\langle w,v \rangle}{\langle w,w \rangle}w \Big\rangle\bigg)

This defect term is much in the spirit of the defect term that we saw in Lagrange’s identity above, and it is instructive to compare them.

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Proof 9: Proof using the AM-GM inequality

Let us first recall the AM-GM inequality:

For non-negative reals x_1, x_2, \dots x_n we have the following basic inequality:

\displaystyle \sqrt[n]{x_1 x_2 \dots x_n} \leq \Big(\frac{x_1 + x_2 + \dots x_n}{n}\Big).

Now let us define \displaystyle A = \sqrt{a_1^2 + a_2^2 + \dots + a_n^2} and \displaystyle B = \sqrt{b_1^2 + b_2^2 + \dots + b_n^2}

Now consider the trivial bound (which gives us the AM-GM): (x-y)^2 \geq 0, which is just \displaystyle \frac{1}{2}\Big(x^2 + y^2\Big) \geq xy. Note that AM-GM as stated above for n = 2 is immediate when we consider x \to \sqrt{x} and y \to \sqrt{y}

Using the above, we have:

\displaystyle \frac{1}{2} \Big(\frac{a_i^2}{A^2} + \frac{b_i^2}{B^2}\Big) \geq \frac{a_ib_i}{AB}

Summing over n, we have:

\displaystyle \sum_{i=1}^n\frac{1}{2} \Big(\frac{a_i^2}{A^2} + \frac{b_i^2}{B^2}\Big) \geq \sum_{i=1}^n \frac{a_ib_i}{AB}

But note that the L.H.S equals 1, therefore:

\displaystyle \sum_{i=1}^n \frac{a_ib_i}{AB} \leq 1 or \displaystyle \sum_{i=1}^n a_ib_i \leq AB

Writing out A and B as defined above, we have:

\displaystyle \sum_{i=1}^n a_ib_i \leq \sqrt{\sum_{i=1}^na_i^2}\sqrt{\sum_{i=1}^nb_i^2}.

Thus proving the Cauchy-Schwarz inequality.

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Proof 10: Using Jensen’s Inequality

We begin by recalling Jensen’s Inequality:

Suppose that f: [p, q] \to \mathbb{R} is a convex function. Also suppose that there are non-negative numbers p_1, p_2, \dots, p_n such that \displaystyle \sum_{i=1}^{n} p_i = 1. Then for all x_i \in [p, q] for i = 1, 2, \dots, n one has:

\displaystyle f\Big(\sum_{i=1}^{n}p_ix_i\Big) \leq \sum_{i=1}^{n}p_if(x_i).

Now we know that f(x) = x^2 is convex. Applying Jensen’s Inequality, we have:

\displaystyle \Big(\sum_{i=1}^{n} p_i x_i \Big)^2 \leq \sum_{i=1}^{n} p_i x_i^2

Now, for b_i \neq 0 for all i = 1, 2, \dots, n, let \displaystyle x_i = \frac{a_i}{b_i} and let \displaystyle p_i = \frac{b_i^2}{\sum_{i=1}^{n}b_i^2}.

Which gives:

\displaystyle \Big(\sum_{i=1}^n \frac{a_ib_i}{\sum_{i=1}^{n}b_i^2}\Big)^2 \leq \Big(\sum_{i=1}^{n}\frac{a_i^2}{\sum_{i=1}^{n}b_i^2}\Big)

Rearranging this just gives the familiar form of Cauchy-Schwarz at once:

\displaystyle \Big(\sum_{i=1}^{n} a_ib_i\Big)^2 \leq \Big(\sum_{i=1}^{n} a_i^2\Big)\Big(\sum_{i=1}^{n} b_i^2\Big)

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Proof 11: Pictorial Proof for d = 2

Here (page 4) is an attractive pictorial proof by means of tilings for the case d = 2 by Roger Nelson.

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The writings (and even papers/technical books) of Gian-Carlo Rota are perhaps amongst the most insightful that I have encountered in the past 3-4 years (maybe even more). Rota wrote to provoke, never resisting to finish a piece of writing with a rhetorical flourish even at the cost of injecting seeming inconsistency in his stance. I guess this is what you get when you have a first rate mathematician and philosopher endowed with an elegant; at times even devastating turn of phrase, with a huge axe to grind*.

The wisdom of G. C. Rota is best distilled in his book of essays, reviews and other thoughts: Indiscrete Thoughts and to some extent Discrete Thoughts. Perhaps I should review Indiscrete Thoughts in the next post, just to revisit some of those writings and my notes from them myself.
Rota in 1962

Rota in 1962

This post however is not about his writing in general as the title indicates. I recently discovered this excellent dialogue between Rota and David Sharp (1985). I found this on a lead from this László Lovász interview. Here he mentions that Rota’s combinatorics papers were an inspiration for him in his work to find more structure in combinatorics. From the David Sharp interview, here are two relevant excerpts (here too the above mentioned flourish is evident):
“Combinatorics is an honest subject. No adèles, no sigma-algebras. You count balls in a box, and you either have the right number or you haven’t. You get the feeling that the result you have discovered is forever, because it’s concrete. Other branches of mathematics are not so clear-cut. Functional analysis of infinite-dimensional spaces is never fully convincing; you don’t get a feeling of having done an honest day’s work. Don’t get the wrong idea – combinatorics is not just putting balls into boxes. Counting finite sets can be a highbrow undertaking, with sophisticated techniques.
[...]
Much combinatorics of our day came out of an extraordinary coincidence. Disparate problems in combinatorics, ranging from problems in statistical mechanics to the problem of coloring a map, seem to bear no common features. However, they do have at least one common feature: their solution can be reduced to the problem of finding the roots of some polynomial or analytic function. The minimum number of colors required to properly color a map is given by the roots of a polynomial, called the chromatic polynomial; its value at N tells you in how many ways you can color the map with N colors. Similarly, the singularities of some complicated analytic function tell you the temperature at which a phase transition occurs in matter. The great insight, which is a long way from being understood, was to realize that the roots of the polynomials and analytic functions arising in a lot of combinatorial problems are the Betti numbers of certain surfaces related to the problem. Roughly speaking, the Betti numbers of a surface describe the number of different ways you can go around it. We are now trying to understand how this extraordinary coincidence comes about. If we do, we will have found a notable unification in mathematics.”
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*While having an axe to grind is a fairly common phrase. I got the idea of using it from an amazon review of Indiscrete thoughts (check link above). Because I really do think that that is the best description for a lot of his writings!

Darwinian Evolution is a form of Computational Learning

The punchline of this book is perhaps: “Changing or increasing functionality of circuits in biological evolution is a form of computational learning“; although it also speaks of topics other than evolution, the underlying framework is of the Probably Approximately Correct model [1] from the theory of Machine Learning, from which the book gets its name.

"Probably Approximately Correct" by Leslie Valiant

“Probably Approximately Correct” by Leslie Valiant

[Clicking on the image above will direct you to the amazon page for the book]

I had first heard of this explicit connection between Machine Learning and Evolution in 2010 and have been quite fascinated by it since. It must be noted, however, that similar ideas have appeared in the past. It won’t be incorrect to say that usually they have been in the form of metaphor. It is another matter that this metaphor has generally been avoided for reasons I underline towards the end of this review. When I first heard about the idea it immediately made sense and like all great ideas, in hindsight looks completely obvious. Ergo, I was quite excited to see this book and preordered it months in advance.

This book is basically a popular science version and expansion of the ideas on the matter that appeared in a J-ACM article titled “Evolvability” in 2007 [2]. I have to say that I was expecting a bit more from the book than what it already had, in this sense I was somewhat disappointed. But at the same time, it can perhaps be a useful ‘starting’ book for those interested in the broad idea but without much background. We all have examples of books like this. Here’s one from me: about a couple of years ago I read a book on Knots by Alexei Sossinsky, the book got bad reviews from many serious mathematicians for not really being about mathematics, besides having many mistakes. But for a complete beginner, I thought it was an absolutely wonderful book, introducing the reader to the basic ideas very informally besides sharing his infectious enthusiasm for problems in Knot Theory. Coming back, in short if you have enough background of the basic notions of PAC Learning then the book might feel quite redundant in some chapters, but depending on how you read, it might still turn out to be a good read any way. Judging from some other (rude) reviews: If you are a professional nitpicker then this book is probably not worth your time since this book deals with ideas and not with a formal treatment of them. Given what it is aiming at, I think it is a good book.

Before attempting to sketch a skiagram of the main content of the book: One of the main subthemes of the book, constantly emphasized is to look at computer science as a kind of an enabling tool to study natural science. This is oft ignored, perhaps because of the reason that CS curricula are rarely designed with any natural science component in them and hence there is no impetus for aspiring computer scientists to view them from the computational lens. On the other hand,  the relation of computer science with mathematics has become quite well established. As a technology the impact of Computer Science has been tremendous. All this is quite remarkable given the fact that just about a century ago the notion of a computation was not even well defined. Unrelated to the book: More recently people have started taking the idea of digital physics (i.e. physics from a solely computable/digital perspective) seriously. But in the other natural sciences its usage is still woeful. Valiant draws upon the example of Alan Turing as a natural scientist and not just as a computer scientist to make this point. Alan Turing was more interested in natural phenomenon (intelligence, limits of mechanical calculation, pattern formation etc) and used tools from Computer Science to study them, a fact that is evident from his publications. That Turing was trying to understand natural phenomenon was remarked in his obituary by Max Neumann by summarizing the body of his work as: “the extent and the limitations of mechanistic explanations of nature”.

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The book begins with a delightful and quite famous quote by John von Neumann (through this paragraph I also discovered the context to the quote). This paragraph also adequately summarizes the motivation for the book very well:

“In 1947 John von Neumann, the famously gifted mathematician, was keynote speaker at the first annual meeting of the Association for Computng Machinery. In his address he said that future computers would get along with just a dozen instruction types, a number known to be adequate for expressing all of mathematics. He went on to say that one need not be surprised at this small number, since 1000 words were known to be adequate for most situations in real life, and mathematics was only a small part of life, and a very simple part at that. The audience responded with hilarity. This provoked von Neumann to respond: “If people do not believe that mathematics is simple, it is only because they do not realize how complicated life is”

Though counterintuitive, von Neumann’s quip contains an obvious truth. Einstein’s theory of general relativity is simple in the sense that one can write the essential content on one line as a single equation. Understanding its meaning, derivation, and consequences requires more extensive study and effort. However, this formal simplicity is striking and powerful. The power comes from the implied generality, that knowledge of one equation alone will allow one to make accurate predictions about a host of situations not even connected when the equation was first written down.

Most aspects of life are not so simple. If you want to succeed in a job interview, or in making an investment, or in choosing a life partner, you can be quite sure that there is no equation that will guarantee you success. In these endeavors it will not be possible to limit the pieces of knowledge that might be relevant to any one definable source. And even if you had all the relevant knowledge, there may be no surefire way of combining it to yield the best decision.

This book is predicated on taking this distinction seriously [...]“

In a way, aspects of life as mentioned above appear theoryless, in the sense that there seems to be no mathematical or scientific theory like relativity for them. Something which is quantitative, definitive and short. Note that these are generally not “theoryless” in the sense that there exists no theory at all since obviously people can do all the tasks mentioned in a complex world quite effectively. A specific example is of how organisms adapt and species evolve without having a theory of the environment as such. How can such coping mechanisms come into being in the first place is the main question asked in the book.

Let’s stick to the specific example of biological evolution. Clearly, it is one of the central ideas in biology and perhaps one of the most important theories in science that changed the way we look at the world. But inspite of its importance, Valiant claims (and correctly in my opinion) that evolution is not understood well in a quantitative sense. Evidence that convinces us of its correctness is of the following sort: Biologists usually show a sequence of evolving objects; stages, where the one coming later is more complex than the previous. Since this is studied mostly via the fossil record there is always a lookout for missing links between successive stages (As a side note: Animal eyes is an extremely fascinating and very readable book that delves with this question but specifically dealing with the evolution of the eye. This is particularly interesting given that due to the very nature of the eye, there can be no fossil records for them). Darwin had remarked that numerous successive paths are necessary – that is, if it was not possible to trace a path from an earlier form to a more complicated form then it was hard to explain how it came about. But again, as Valiant stresses, this is not really an explanation of evolution. It is more of an “existence proof” and not a quantitative explanation. That is, even if there is evidence for the existence of a path, one can’t really say that a certain path is likely to be followed just because it exists. As another side note: Watch this video on evolution by Carl Sagan from the beautiful COSMOS

Related to this, one of the first questions that one might ask, and indeed was asked by Darwin himself: Why has evolution taken as much time as it has? How much time would have sufficed for all the complexity that we see around us to evolve? This question infact bothered Darwin a lot in his day. In On the Origins of Species he originally gave an estimate of the Earth’s age to be at least 300 million years, implying indirectly, that there was enough time. This estimate was immediately thrashed by Lord Kelvin, one of the pre-eminent British physicists of the time, who estimated the age of the Earth to be only about 24 million years. This caused Darwin to withdraw the estimate from his book. However, this estimate greatly worried Darwin as he thought 24 million years just wasn’t enough time. To motivate on the same theme Valiant writes the following:

“[...] William Paley, in a highly influential book, Natural Theology (1802) , argued that life, as complex as it is, could not have come into being without the help of a designer. Numerous lines of evidence have become available in the two centuries since, through genetics and the fossil record, that persuade professional biologists that existing life forms on Earth are indeed related and have indeed evolved. This evidence contradicts Paley’s conclusion, but it does not directly address his argument. A convincing direct counterargument to Paley’s would need a specific evolution mechanism to be demonstrated capable of giving rise to the quantity and quality of the complexity now found in biology, within the time and resources believed to have been available. [...]“

A specific, albeit more limited version of this question might be: Consider the human genome, which has about 3 billion base pairs. Now, if evolution is a search problem, as it naturally appears to be, then why did the evolution of this genome not take exponential time? If it would have taken exponential time then clearly such evolution could not have happened in any time scale since the origin of the earth. Thus, a more pointed question to ask would be: What circuits could evolve in sub-exponential time (and on a related note, what circuits are evolvable only in exponential time?). Given the context, the idea of thinking about this in circuits might seem a little foreign. But on some more thought it is quite clear that the idea of a circuit is natural when it comes to modeling such systems (at least in principle). For example: One might think of the genome as a circuit, just as how one might think of the complex protein interaction networks and networks of neurons in the brain as circuits that update themselves in response to the environment.

The last line is essentially the key idea of adaptation, that entities interact with the environment and update themselves (hopefully to cope better) in response. But the catch is that the world/environment is far too complex for simple entities (relatively speaking), with limited computational power, to have a theory for. Hence, somehow the entity will have to cope without really “understanding” the environment (it can only be partially modeled) and improve their functionality. The key thing to pay attention here is the interaction or the interface between the limited computational entity in question and the environment. The central idea in Valiant’s thesis is to think of and model this interaction as a form of computational learning. The entity will absorb information about the world and “learn” so that in the future it “can do better”. A lot of Biology can be thought of as the above: Complex behaviours in environments. Wherein by complex behaviours we mean that in different circumstances, we (or alternately our limited computational entity) might behave completely differently. Complicated in the sense that there can’t possibly be a look up table for modeling such complex interactions. Such interactions-responses can just be thought of as complicated functions e.g. how would a deer react could be seen as a function of some sensory inputs. Or for another example: Protein circuits. The human body has about 25000 proteins. How much of a certain protein is expressed is a function depending on the quantities of other proteins in the body.  This function is quite complicated, certainly not something like a simple linear regression. Thus there are two main questions to be asked: One, how did these circuits come into being without there being a designer to actually design them? Two, How do such circuits maintain themselves? That is, each “node” in protein circuits is a function and as circumstances change it might be best to change the way they work. How could have such a thing evolved?

Given the above, one might ask another question: At what rate can functionality increase or adapt under the Darwinian process? Valiant (like many others, such as Chaitin. See this older blog post for a short discussion) comes to the conclusion that the Darwinian evolution is a very elegant computational process. And since it is so, with the change in the environment there has to be a quantitative way of saying how much rate of change can be kept up with and what environments are unevolvable for the entity. It is not hard to see that this is essentially a question in computer science and no other discipline has the tools to deal with it.

In so far that (biological) interactions-responses might be thought of as complicated functions and that the limited computational entity that is trying to cope has to do better in the future, this is just machine learning! This idea, that changing or increasing functionality of circuits in biological evoution is a form of computational learning, is perhaps very obvious in hindsight. This (changing functionality) is done in Machine Learning in the following sense: We want to acquire complicated functions without explicitly programming for them, from examples and labels (or “correct” answers). This looks at exactly at the question at how complex mechanisms can evolve without someone designing it (consider a simple perceptron learning algorithm for a toy example to illustrate this). In short: We generate a hypothesis and if it doesn’t match our expectations (in performance) then we update the hypothesis by a computational procedure. Just based on a single example one might be able to change the hypothesis. One could draw an analogy to evolution where “examples” could be experiences, and the genome is the circuit that is modified over a period of time. But note that this is not how it looks like in evolution because the above (especially drawing to the perceptron example) sounds more Lamarckian. What the Darwinian process says is that we don’t change the genome directly based on experiences. What instead happens is that we make a lot of copies of the genome which are then tested in the environment with the better one having a higher fitness. Supervised Machine Learning as drawn above is very lamarckian and not exactly Darwinian.

Thus, there is something unsatisfying in the analogy to supervised learning. There is a clear notion of a target in the same. Then one might ask, what is the target of evolution? Evolution is thought of as an undirected process. Without a goal. This is true in a very important sense however this is incorrect. Evolution does not have a goal in the sense that it wants to evolve humans or elephants etc. But it certainly does have a target. This target is “good behaviour” (where behaviour is used very loosely) that would aid in the survival of the entity in an environment. Thus, this target is the “ideal” function (which might be quite complicated) that tells us how to behave in what situation. This is already incorporated in the study of evolution by notions such as fitness that encode that various functions are more beneficial. Thus evolution can be framed as a kind of machine learning problem based on the notion of Darwinian feedback. That is, make many copies of the “current genome” and let the one with good performance in the real world win. More specifically, this is a limited kind of PAC Learning.  If you call your current hypothesis your genome, then your genome does not depend on your experiences. Variants to this genome are generated by a polynomial time randomized Turing Machine. To illuminate the difference with supervised learning, we come back to a point made earlier that PAC Learning is essentially Lamarckian i.e. we have a hidden function that we want to learn. One however has examples and labels corresponding to this hidden function, these could be considered “queries” to this function. We then process these example/label pairs and learn a “good estimate” of this hidden function in polynomial time. It is slightly different in Evolvability. Again, we have a hidden “ideal” function f(x). The examples are genomes. However, how we can find out more about the target is very limited, since one can empirically only observe the aggregate goodness of the genome (a performance function). The task then is to mutate the genome so that it’s functionality improves. So the main difference with the usual supervised learning is that one could query the hidden function in a very limited way: That is we can’t act on a single example and have to take aggregate statistics into account.

Then one might ask what can one prove using this model? Valiant demonstrates some examples. For example, Parity functions are not evolvable for uniform distribution while monotone disjunctions are actually evolvable. This function is ofcourse very non biological but it does illustrate the main idea: That the ideal function together with the real world distribution imposes a fitness landscape and that in some settings we can show that some functions can evolve and some can not. This in turn illustrates that evolution is a computational process independent of substrate.

In the same sense as above it can be shown that Boolean Conjunctions are not evolvable for all distributions. There has also been similar work on real valued functions off late which is not reported in detail in the book. Another recent work that is only mentioned in passing towards the end is the study of multidimensional space of actions that deal with sets of functions (not just one function) that might be evolvable together. This is an interesting line of work as it is pretty clear that biological evolution deals with the evolution of a set of functions together and not functions in isolation.

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Overall I think the book is quite good. Although I would rate it 3/5. Let me explain. Clearly this book is aimed at the non expert. But this might be disappointing to those who bought the book because of the fact that this recent area of work, of studying evolution through the lens of computational learning, is very exciting and intellectually interesting. The book is also aimed at biologists, and considering this, the learning sections of the book are quite dumbed down. But at the same time, I think the book might fail to impress most of them any way. I think this is because generally biologists (barring a small subset) have a very different way of thinking (say as compared to the mathematicians or computer scientists) especially through the computational lens. I have had some arguments about the main ideas in the book over the past couple of years with some biologist friends who take the usage of “learning” to mean that what is implied is that evolution is a directed process. It would have been great if the book would have spent more time on this particular aspect. Also, the book explicitly states that it is about quantitative aspects of evolution and has nothing to do with speciation, population dynamics and other rich areas of study. However, I have already seen some criticism of the book by biologists on this premise.

As far as I am concerned, as an admirer of Prof. Leslie Valiant’s range and quality of contributions, I would have preferred if the book went into more depth. Just to have a semi-formal monograph on the study of evolution using the tools of PAC Learning right from the person who initiated this area of study. However this is just a selfish consideration.

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References:

[1] A Theory of the Learnable, Leslie Valiant, Communications of the ACM, 1984 (PDF).

[2] Evolvability, Leslie Valiant, Journal of the ACM, 2007 (PDF).

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On a humorous note:

“In every chaos there is an order: A very good writer wrote a rather nice book. But there was no title to it yet. He asked his friend for suggestions. The friend asked him if there was either a drum or a guitar mentioned in the book, to which the author replied in the negative. The book then had the title: A story without drums and guitars.”

(Endre Szemerédi, slightly paraphrased)

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