Tag Archives: Analysis

Intra-mathematical Dependencies

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Recently I completed all of my undergraduate level maths courses, so wanted to sum up my understanding of mathematics in the following dependency diagram:

mat-dependency (1)

I imagine this like a wall, where each topic is a brick. You can bake different bricks at different times (i.e. follow your curriculum to learn these topics), but finally, this is how they should be arranged (in my opinion) to get the best possible understanding of mathematics.

As of now, I have an “elementary” knowledge of Set Theory, Algebra, Analysis, Topology, Geometry, Probability Theory, Combinatorics and Arithmetic. Unfortunately, in India, there are no undergraduate level courses in Mathematical Logic and Category Theory.

This post can be seen as a sequel of my “Mathematical Relations” post.

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In the praise of norm

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If you have spent some time with undergraduate mathematics, you would have probably heard the word “norm”. This term is encountered in various branches of mathematics, like (as per Wikipedia):

But, it seems to occur only in abstract algebra. Although the definition of this term is always algebraic, it has a topological interpretation when we are working with vector spaces.  It secretly connects a vector space to a topological space where we can study differentiation (metric space), by satisfying the conditions of a metric.  This point of view along with an inner product structure, is explored when we study functional analysis.

Some facts to remember:

  1. Every vector space has a norm. [Proof]
  2. Every vector space has an inner product (assuming “Axiom of Choice”). [Proof]
  3. An inner product naturally induces an associated norm, thus an inner product space is also a normed vector space.  [Proof]
  4. All norms are equivalent in finite dimensional vector spaces. [Proof]
  5. Every normed vector space is a metric space (and NOT vice versa). [Proof]
  6. In general, a vector space is NOT same a metric space. [Proof]

Real vs Complex numbers

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I want to talk about the algebraic and analytic differences between real and complex numbers. Firstly, let’s have a look at following beautiful explanation by Richard Feynman (from his QED lectures) about similarities between real and complex numbers:

img_20170304_1237442

From Chapter 2 of the book “QED – The Strange Theory of Light and Matter” © Richard P. Feynman, 1985.

Before reading this explanation, I used to believe that the need to establish “Fundamental theorem Algebra” (read this beautiful paper by Daniel J. Velleman to learn about proof of this theorem) was only way to motivate study of complex numbers.

The fundamental difference between real and complex numbers is

Real numbers form an ordered field, but complex numbers can’t form an ordered field. [Proof]

Where we define ordered field as follows:

Let \mathbf{F} be a field. Suppose that there is a set \mathcal{P} \subset \mathbf{F} which satisfies the following properties:

  • For each x \in \mathbf{F}, exactly one of the following statements holds: x \in \mathcal{P}, -x \in \mathcal{P}, x =0.
  • For x,y \in \mathcal{P}, xy \in \mathcal{P} and x+y \in \mathcal{P}.

If such a \mathcal{P} exists, then \mathbf{F} is an ordered field. Moreover, we define x \le y \Leftrightarrow y -x \in \mathcal{P} \vee x = y.

Note that, without retaining the vector space structure of complex numbers we CAN establish the order for complex numbers [Proof], but that is useless. I find this consequence pretty interesting, because though \mathbb{R} and \mathbb{C} are isomorphic as additive groups (and as vector spaces over \mathbb{Q}) but not isomorphic as rings (and hence not isomorphic as fields).

Now let’s have a look at the consequence of the difference between the two number systems due to the order structure.

Though both real and complex numbers form a complete field (a property of topological spaces), but only real numbers have least upper bound property.

Where we define least upper bound property as follows:

Let \mathcal{S} be a non-empty set of real numbers.

  • A real number x is called an upper bound for \mathcal{S} if x \geq s for all s\in \mathcal{S}.
  • A real number x is the least upper bound (or supremum) for \mathcal{S} if x is an upper bound for \mathcal{S} and x \leq y for every upper bound y of \mathcal{S} .

The least-upper-bound property states that any non-empty set of real numbers that has an upper bound must have a least upper bound in real numbers.
This least upper bound property is referred to as Dedekind completeness. Therefore, though both \mathbb{R} and \mathbb{C} are complete as a metric space [proof] but only \mathbb{R} is Dedekind complete.

In an arbitrary ordered field one has the notion of Dedekind completeness — every nonempty bounded above subset has a least upper bound — and also the notion of sequential completeness — every Cauchy sequence converges. The main theorem relating these two notions of completeness is as follows [source]:

For an ordered field \mathbf{F}, the following are equivalent:
(i) \mathbf{F} is Dedekind complete.
(ii) \mathbf{F} is sequentially complete and Archimedean.

Where we defined an Archimedean field as an ordered field such that for each element there exists a finite expression 1+1+\ldots+1 whose value is greater than that element, that is, there are no infinite elements.

As remarked earlier, \mathbb{C} is not an ordered field and hence can’t be Archimedean. Therefore, \mathbb{C}  can’t have least-upper-bound property, though it’s complete in topological sense. So, the consequence of all this is:

We can’t use complex numbers for counting.

But still, complex numbers are very important part of modern arithmetic (number-theory), because they enable us to view properties of numbers from a geometric point of view [source].

Area of Rectangle

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When we learn to find area enclosed by a curve, we are told to divide area in rectangular elements.

integratuon

Taken from pp. 361 of Mathematics (Part – II), Class XII textbook, NCERT

But, how do we always know the value of area of rectangle? In this post, I will try to prove this well known fact in the spirit of Euclid and Cauchy.

Let’s define:

  • Boundary: A boundary is that which is an extremity of anything.
  • Figure: A figure is that which is contained by any boundary or boundaries.
  • Triangular region : A triangular region is a figure which is the union of a triangle and its interior. Also the sides of the triangle are called edges of the region and vertices of the triangle are called vertices of the region.
  • Polygonal region: A polygonal region is a plane figure which can be expressed as the
    union of a finite number of triangular regions, in such a way that if two of the
    triangular regions intersect, their intersection is an edge or a vertex of each of them.
  • Square region: It is the union of a square and its interior.

The structure in our geometry is

[\mathcal{S},\mathcal{L}, \mathcal{P}, d, m, \alpha]

where \mathcal{S} is the set of points, \mathcal{L} is the set of lines, \mathcal{P} is the set of planes, d is distance (a function satisfying first three properties of metric function), m is angular measure (a function defined for angles, with real numbers as values of the function, satisfying following postulates) and \alpha is the area function satisfying following postulates:

  1. \alpha is a function \mathcal{R} \rightarrow \mathbb{R}, where \mathcal{R} is the set of all polygonal regions and \mathbb{R}  is the set of all real numbers.
  2. For every polygonal region R, \alpha(R) > 0.
  3. If two triangular regions are congruent, then they have the same area.
  4. If two polygonal regions intersect only in edges and vertices (or do not intersect at all), then the area of their union is the sum of their areas.
  5. If a square region has edges of length 1, then its area is 1.

Proposition 1. If a square has edges of length 1/q (q a positive integer), then its area is 1/q^2.

Proof. A unit square region can be decomposed into q^2 square regions, all with the same edge 1/q as

sq1

Then all smaller square have the same area A (divide each square into triangles using diagonals and then use Postulate 3 to prove that all of them have same area). Therefore 1 = q^2A (from Postulate 4) and A = 1/q^2.

Proposition 2. If a square has edges of rational length p/q, then its area is p^2/q^2.

Proof. Such a square can be decomposed into p^2 squares, each of edge 1/q as:

sq2.png

If A is its area, then

A = p^2 \times \frac{1}{q^2} = \frac{p^2}{q^2}

Proposition 3.[Peter Lawes] If a square has edges of length a, then its area is a^2.

Proof. Given a square S_a with edges of length a. Given any rational number p/q,  let S_{p/q} be a square of edge p/q, with an angle in common with S_a, as:

sq3

Then, \frac{p}{q} < a hence S_{p/q} lies inside S_a. For some real number s (by using Postulate 4) we get:

\alpha(S_{p/q}) + s = \alpha(S_{a})

\Rightarrow \alpha(S_{p/q}) < \alpha(S_{a})

\Rightarrow \frac{p^2}{q^2}< \alpha(S_{a})

\Rightarrow\frac{p}{q} < \sqrt{\alpha(S_{a})}

But, selection of \frac{p}{q} being arbitrary, the upper-bound should be unique. Since there exists a unique supremum of the set consisting all possible side lengths of smaller square in \mathbb{R}, we can claim:

a = \sqrt{\alpha(S_{a})}

We can prove this claim by following the proof of statement: \sup\{x \in \mathbb{R} : 0 \leq x, x^2 < 2\} = \sqrt{2}. Hence:

a^2=\alpha(S_a)

Theorem: Area of rectangle is equal to the product of length of any two adjacent sides.

Proof. Given a rectangle of base b and altitude h, we construct a square of edge b + h, and decompose it into squares and rectangles as:

rect
Then from Postulate 4, we get:

(b+h)^2 = 2A + A_1 +A_2

b^2 + 2bh + h^2=2A +h^2+b^2

2bh = 2A

bh = A

 

REFERENCE:

Moise, Edwin (1990). Elementary Geometry from an Advanced Standpoint. Addison-Wesley Pub. Co.

Thanks to Dr. Shailesh Shirali for pointing out this beautiful book.

 

So many Integrals – II

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As promised in previous post, now I will briefly discuss the remaining two flavors of Integrals.

Stieltjes Integral

ss

Stieltjes

In 1894, a Dutch mathematician, Thomas Stieltjes, while solving the moment problem, that is, given the moments of all orders of a body, find the distribution of its mass, gave a generalization of the Darboux integral.

Let P : a = x_0 < x_1 < x_2<\ldots < x_n = b, n being an integer, be a partition of the interval [a, b].

For a function \alpha, monotonically increasing on [a,b], we write:

\Delta \alpha_i = \alpha(x_i) - \alpha(x_{i-1})

Let f be a bounded function defined on an interval [a, b],\quad a, b being real numbers. We define the sum 

S_P = \sum_{i=1}^n f(t_i)\Delta \alpha_i, \quad \overline{S}_P = \sum_{i=1}^n f(s_i)\Delta \alpha_i

where t_i,s_i \in [x_{i-1} , x_i] be such that

f(t_i) = \text{sup} \{ f(x) : x \in [x_{i-1}, x_{i}]\},

f(s_i) = \text{inf} \{ f(x) : x \in [x_{i-1}, x_{i}]\}

If the \text{inf}\{S_P\} and \text{sup}\{\overline{S}_P\} are equal, we denote the common value by  \int_{a}^{b} f(x) d\alpha(x) and call it Steiltjes integral of f with respect to \alpha over [a,b].


 

Lebesgue Integral

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Lebesgue

Let me quote Wikipedia article:

The integral of a function f between limits a and b can be interpreted as the area under the graph of f. This is easy to understand for familiar functions such as polynomials, but what does it mean for more exotic functions? In general, for which class of functions does “area under the curve” make sense? The answer to this question has great theoretical and practical importance.

In 1901, a French mathematician, Henri Léon Lebesgue generalized the notion of the integral by extending the concept of the area below a curve to include functions with uncountable discontinuities .

Lebesgue defined his integral by partitioning the range of a function and summing up sets of x-coordinates belonging to given y-coordinates, rather than, as had traditionally been done, partitioning the domain.

Lebesgue himself, according to his colleague, Paul Montel, compared his method with paying off a debt: (see:pp. 803,  The Princeton Companion to Mathematics)

I have to pay a certain sum, which I have collected in my pocket. I take the bills and coins out of my pocket and give them to the creditor in the order I find them until I have reached the total sum. This is the Riemann integral. But I can proceed differently. After I have taken all the money out of my pocket I order the bills and coins according to identical values and then I pay the several heaps one after the other to the creditor. This is my integral.

A set \mathcal{A} is said to be Lebesgue measurable, if for each set \mathcal{E} \subset \mathbb{R} the Carathéodory condition:

m^{*} (\mathcal{E}) = m^{*}(\mathcal{E} \cap \mathcal{A}) + m^{*}(\mathcal{E}\backslash \mathcal{A})

is satisfied, where m^{*}(\mathcal{A}) is called outer measure and is defined as:

m^{*}(\mathcal{A}) = \inf\sum\limits_{n=1}^\infty (b_n-a_n)

where \mathcal{A} is a countable collection of closed intervals [a_n,b_n], a_n\leq b_n, that cover \mathcal{A}.

The Lebesgue integral of a simple function \phi(x) = \sum_{i=1}^n c_i \chi_{\mathcal{A}_i} (x) on \mathcal{A}, where \mathcal{A}=\bigcup_{i=1}^{\infty} \mathcal{A}_{i}, \mathcal{A}_i are pairwise disjoint measurable sets and c_1, c_2, \ldots are real numbers, is defined as:

\int\limits_{\mathcal{A}} \phi dm = \sum\limits_{i=1}^{n} c_i m(\mathcal{A}_i)

where, m(\mathcal{A}_i) is the Lebesgue measure of a measurable set \mathcal{A}_i.

An extended real value function f: \mathcal{A}\rightarrow \overline{\mathbb{R}} defined on a measurable set \mathcal{A}\subset\mathbb{R} is said to be Lebesgue measurable on \mathcal{A} if f^{-1} ((c,\infty]) = \{x \in\mathcal{A} : f(x) > c\} is a Lebesgue measurable subset of \mathcal{A} for every real number c.

If f is Lebesgue measurable and non-negative on \mathcal{A} we define:

\int\limits_{\mathcal{A}} f dm = \sup \int\limits_{\mathcal{A}} \phi dm

where the supremum is taken over all simple functions \phi such that 0\leq \phi \leq f.

The function f is said to be Lebesgue integrable on \mathcal{A} if it’s integral over \mathcal{A} is finite.


The Lebesgue integral is deficient in one respect. The Riemann integral generalizes to the improper Riemann integral to measure functions whose domain of definition is not a closed interval. The Lebesgue integral integrates many of these functions, but not all of them.

So many Integrals – I

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So many Integrals – I

We all know that, area is  the basis of integration theory, just as counting is basis of the real number system. So, we can say:

An integral is a mathematical operator that can be interpreted as an area under curve.

But, in mathematics we have various flavors of integrals named after their discoverers. Since the topic is a bit long, I have divided it into two posts. In this and next post I will write their general form and then will briefly discuss them.

Cauchy Integral

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Newton, Leibniz and Cauchy (left to right)

This was rigorous formulation of Newton’s & Leibniz’s idea of integration, in 1826 by French mathematician, Baron Augustin-Louis Cauchy.

Let f be a positive continuous function defined on an interval [a, b],\quad a, b being real numbers. Let P : a = x_0 < x_1 < x_2<\ldots < x_n = b, n being an integer, be a partition of the interval [a, b] and form the sum

S_p = \sum_{i=1}^n (x_i - x_{i-1}) f(t_i)

where t_i \in [x_{i-1} , x_i]f be such that f(t_i) = \text{Minimum} \{ f(x) : x \in [x_{i-1}, x_{i}]\}

By adding more points to the partition P, we can get a new partition, say P', which we call a ‘refinement’ of P and then form the sum S_{P'}.  It is trivial to see that S_P \leq S_{P'} \leq \text{Area bounded between x-axis and function}f

Since, f is continuous (and positive), then S_P becomes closer and closer to a unique real number, say kf, as we take more and more refined partitions in such a way that |P| := \text{Maximum} \{x_i - x_{i-1}, 1 \leq i \leq n\} becomes closer to zero. Such a limit will be independent of the partitions. The number k is the area bounded by function and x-axis and we call it the Cauchy integral of f over a  to b. Symbolically, \int_{a}^{b} f(x) dx (read as “integral of f(x)dx from a to b”).


 

Riemann Integral

Riemann_3

Riemann

Cauchy’s definition of integral can readily be extended to a bounded function with finitely many discontinuities. Thus, Cauchy integral does not require either the assumption of continuity or any analytical expression of f to prove that the sum S_p indeed converges to a unique real number.

In 1851, a German mathematician, Georg Friedrich Bernhard Riemann gave a more general definition of integral.

Let [a,b] be a closed interval in \mathbb{R}. A finite, ordered set of points P :\{ a = x_0 < x_1 < x_2<\ldots < x_n = b\}, n being an integer, be a partition of the interval [a, b]. Let, I_j denote the interval [x_{j-1}, x_j], j= 1,2,3,\ldots , n. The symbol \Delta_j denotes the length of I_j. The mesh of P, denoted by m(P), is defined to be max\Delta_j.

Now, let f be a function defined on interval [a,b]. If, for each j, s_j is an element of I_j, then we define:

S_P = \sum_{j=1}^n f(s_j) \Delta_j

Further, we say that S_P tend to a limit k as m(P) tends to 0 if, for any \epsilon > 0, there is a \delta >0 such that, if P is any partition of [a,b] with m(P) < \delta, then |S_P - k| < \epsilon for every choice of s_j \in I_j.

Now, if S_P tends to a finite limit as m(P) tends to zero, the value of the limit is called Riemann integral of f over [a,b] and is denoted by \int_{a}^{b} f(x) dx


 

Darboux Integral

Darboux.jpg

Darboux

In 1875, a French mathematician, Jean Gaston Darboux  gave his way of looking at the Riemann integral, defining upper and lower sums and defining a function to be integrable if the difference between the upper and lower sums tends to zero as the mesh size gets smaller.

Let f be a bounded function defined on an interval [a, b],\quad a, b being real numbers. Let P : a = x_0 < x_1 < x_2<\ldots < x_n = b, n being an integer, be a partition of the interval [a, b] and form the sum

S_P = \sum_{i=1}^n (x_i - x_{i-1}) f(t_i), \quad \overline{S}_P =\sum_{i=1}^n (x_i - x_{i-1}) f(s_i)

where t_i,s_i \in [x_{i-1} , x_i] be such that

f(t_i) = \text{sup} \{ f(x) : x \in [x_{i-1}, x_{i}]\},

f(s_i) = \text{inf} \{ f(x) : x \in [x_{i-1}, x_{i}]\}

The sums S_P and \overline{S}_P represent the areas and  S_P \leq \text{Area bounded by curve} \leq \overline{S}_P. Moreover, if P' is a refinement of P, then

S_p \leq S_{P'} \leq \text{Area bounded by curve} \leq \overline{S}_{P'} \leq \overline{S}_{P}

Using the boundedness of f, one can show that S_P, \overline{S}_P converge as the partition get’s finer and finer, that is |P| := \text{Maximum}\{x_i - x_{i-1}, 1 \leq i \leq n\} \rightarrow 0, to some real numbers, say k_1, k_2 respectively. Then:

k_l \leq \text{Area bounnded by the curve} \leq k_2

If k_l = k_2 , then we have \int_{a}^{b} f(x) dx = k_l = k_2.


There are two more flavours of integrals which I will discuss in next post. (namely, Stieltjes Integral and Lebesgue Integral)

What is Analysis?

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I do not know how to re-produce proofs in Analysis exams, but in this post I will try to know why we study Analysis. Most of us believe that Analysis is same as rigorous Calculus. Also, what makes Mathematics different from Physics is the “rigour”. But, why mathematicians worry so much about rigour? To understand answers of this question one need to understand, what is called “Analysis” in mathematics?

A standard definition of Analysis is (as in [R]):

Analysis is the systematic study of real and complex-valued continuous functions.

The above definition tells us what we will achieve by application of our understanding of Analysis, but this doesn’t explains what “Analysis” itself is.

Clearly, analysis has its roots in calculus. Newton and Leibniz defined differentiation and integration without bothering about definition of limit. Euler found correct value of limit of various infinite series by implicitly assuming “Algebra of infinite series”, which doesn’t exist! I myself used the commutativity of addition of real numbers for the terms in infinite series by assuming “Algebra of infinite series”!! Great mathematicians like Euler, Laplace etc. who even solved differential equations never bothered to think about foundations of calculus because they studied only real variable functions arising from physical problems and series which are power series.

Though without bothering about foundations, we could easily (intuitively) arrive at correct answers due to deep insights (of great mathematicians) but it became extremely difficult to teach such “deep insight” based mathematics to students. Without sense of rigour it became difficult to prove our claims for general cases (like the difference between point-wise continuity and uniform continuity).
This lead to a belief that:

Calculus (and thus Mathematics) is as good as theory of ghosts i.e. without any basis.

Also it became impossible for mathematicians to apply techniques of calculus beyond physical situations i.e. generalization of concepts was not possible.

To get rid of such allegations, Lagrange suggested that the only way to make calculus rigorous is to reduce it to Algebra (since algebra has inherent power of generalization). To illustrate this he defines derivative of a real function, f'(x) as coefficient of the linear term in h in Taylor series expansion for f(x+h). Again this was wrong without consideration of limits and convergence, since there is no “Algebra of infinite series”!!! But this idea of using Algebra to make calculus rigorous was successfully realized by Cauchy, he used “Algebra of Inequalities” (but he also implicitly assumed the completeness property of real numbers) by introducing \epsilon and \delta (though not explicitly, but in words).

How “Algebra of Inequalities” became technique to create “rigorous calculus”, which we know as “Analysis” ? One main part of calculus was “Approximations”, i.e. to compute an upper bound on the error in the approximation — that is, the difference between the sum of the series and the n^{th} partial sum. Thus the “Tool of Approximation” was transformed to “tool of rigour”.

Initially, integral was thought as inverse of differential. But sometimes the inverse could not be computed exactly, so Euler remarked that the integral could be approximated as closely as one liked by a sum (also the geometric picture of an area being approximated by rectangles). Again, we got better definition of integral by work done by various mathematicians to approximate the values of definite integrals. Poisson, was interested in complex integration and was concerned about behaviour and existence of integrals. He stated and proved  “The fundamental proposition of the theory of definite integrals”. He proved it by using an inequality-result: the Taylor series with remainder. This was the first attempt to prove the equivalence of the antiderivative and limit-of-sums conceptions of the integral. But, Poission implicitly assumes the existence of antiderivatives and bounded first derivatives for f on the given interval, thus the proof assumes that the subintervals on which the sum is taken are all equal. Again, Cauchy added rigour to Poisson’s proof.

Since most algebraic formulas hold only under certain conditions, and for certain values of the quantities they contain, one could not assume that what worked for finite expressions automatically worked for infinite ones. Also,  just because there was an operation called “taking a derivative” did not mean that the inverse of that operation always produced a result. The existence of the definite integral had to be proved. Borrowing from Lagrange the mean value theorem for integrals, Cauchy finally proved the “Fundamental Theorem of Calculus”.

Thus, algebraic approximations produced the algebra of inequalities. The application of Algebra of inequalities lead to concept of Approximations in Calculus. The concept of approximations in calculus in turn lead to 3 key concepts : “error bounds for series” (d’Alembert), “inequalities about derivatives” (Lagrange) and “approximations to integrals” (Euler). I believe that, these three concepts combined with rigour lead to what we call “Analysis” in Mathematics.

References:
[G] J V Grabiner, “Who Gave You the Epsilon? Cauchy and the Origins of Rigorous Calculus”, American Mathematical Monthly 90 (1983), 185–194

[R] John Renze and Eric W. Weisstein, “Analysis.” From MathWorld–A Wolfram Web Resource. http://mathworld.wolfram.com/Analysis.html

[S] Ian Stewart,  “analysis | mathematics”. Encyclopedia Britannica.
http://www.britannica.com/topic/analysis-mathematics

[X] Mathematical analysis. Encyclopedia of Mathematics. URL: http://www.encyclopediaofmath.org/index.php?title=Mathematical_analysis&oldid=31489