From Wikipedia, the free encyclopedia
A hierarchy of mathematical spaces: The inner product induces a norm. The norm induces a metric. The metric induces a topology.
In
mathematics, a
space is a
set (sometimes called a
universe) with some added
structure.
Mathematical spaces often form a hierarchy, i.e., one space may
inherit all the characteristics of a parent space. For instance, all
inner product spaces are also
normed vector spaces, because the inner product
induces a
norm on the inner product space such that:
where the norm is indicated by enclosing in double vertical lines,
and the inner product is indicated enclosing in by angle brackets.
Modern mathematics treats "space" quite differently compared to classical mathematics.
History
Before the golden age of geometry
In
the ancient mathematics, "space" was a geometric abstraction of the
three-dimensional space observed in everyday life. About 300 BC,
Euclid
gave axioms for the properties of space. Euclid built all of
mathematics on these geometric foundations, going so far as to define
numbers by comparing the lengths of line segments to the length of a
chosen reference segment.
The method of coordinates (
analytic geometry) was adopted by
René Descartes in 1637.
[1]
At that time, geometric theorems were treated as an absolute objective
truth knowable through intuition and reason, similar to objects of
natural science;
[2]:11 and axioms were treated as obvious implications of definitions.
[2]:15
Two
equivalence relations between geometric figures were used:
congruence and
similarity. Translations, rotations and reflections transform a figure into congruent figures;
homotheties
— into similar figures. For example, all circles are mutually similar,
but ellipses are not similar to circles. A third equivalence relation,
introduced by
Gaspard Monge in 1795, occurs in
projective geometry:
not only ellipses, but also parabolas and hyperbolas, turn into circles
under appropriate projective transformations; they all are projectively
equivalent figures.
The relation between the two geometries, Euclidean and projective,
[2]:133 shows that mathematical objects are not given to us
with their structure.
[2]:21 Rather, each mathematical theory describes its objects by
some of their properties, precisely those that are put as axioms at the foundations of the theory.
[2]:20
Distances and angles are never mentioned in the axioms of the
projective geometry and therefore cannot appear in its theorems. The
question "what is the sum of the three angles of a triangle" is
meaningful in the Euclidean geometry but meaningless in projective
geometry.
A different situation appeared in the 19th century: in some
geometries the sum of the three angles of a triangle is well-defined but
different from the classical value (180 degrees). The non-Euclidean
hyperbolic geometry, introduced by
Nikolai Lobachevsky in 1829 and
János Bolyai in 1832 (and
Carl Gauss in 1816, unpublished)
[2]:133 stated that the sum depends on the triangle and is always less than 180 degrees.
Eugenio Beltrami in 1868 and
Felix Klein in 1871 obtained Euclidean "models" of the non-Euclidean
hyperbolic geometry, and thereby completely justified this theory.
[2]:24[3]
This discovery forced the abandonment of the pretensions to the
absolute truth of Euclidean geometry. It showed that axioms are not
"obvious", nor "implications of definitions". Rather, they are
hypotheses. To what extent do they correspond to an experimental
reality? This important physical problem no longer has anything to do
with mathematics. Even if a "geometry" does not correspond to an
experimental reality, its theorems remain no less "mathematical truths".
[2]:15
A Euclidean model of a
non-Euclidean geometry
is a clever choice of some objects existing in Euclidean space and some
relations between these objects that satisfy all axioms (therefore, all
theorems) of the non-Euclidean geometry. These Euclidean objects and
relations "play" the non-Euclidean geometry like contemporary actors
playing an ancient performance. Relations between the actors only mimic
relations between the characters in the play. Likewise, the chosen
relations between the chosen objects of the Euclidean model only mimic
the non-Euclidean relations. It shows that relations between objects are
essential in mathematics, while the nature of the objects is not.
The golden age and afterwards: dramatic change
According to
Nicolas Bourbaki,
[2]:131 the period between 1795 (
Geometrie descriptive of Monge) and 1872 (the "
Erlangen programme" of Klein) can be called the golden age of geometry. The original space investigated by Euclid is now called three-dimensional
Euclidean space. Its axiomatization, started by Euclid 23 centuries ago, was reformed with
Hilbert's axioms,
Tarski's axioms and
Birkhoff's axioms. These axiom systems describe space via
primitive notions (such as "point", "between", "congruent") constrained by a number of
axioms.
Analytic geometry made great progress, and it succeeded in replacing
theorems of classical geometry with computations via invariants of
transformation groups.
[2]:134,5
This technique applies simultaneously to many geometries. A definition
of space "from scratch", as in Euclid, is now not often used since it
does not reveal the relation of this space to other spaces. Since this
time, new theorems of classical geometry are of more interest to
amateurs rather than to professional mathematicians.
[2]:136 However, the heritage of the classical geometry was not lost. According to Bourbaki,
[2]:138
"passed over in its role as an autonomous and living science, classical
geometry is thus transfigured into a universal language of contemporary
mathematics".
Simultaneously, numbers began to displace geometry as the foundation
of mathematics. For instance, in Richard Dedekind's 1872 essay
Stetigkeit und irrationale Zahlen (
Continuity and irrational numbers), he asserts that points on a line ought to have the properties of
Dedekind cuts,
and that therefore a line was the same thing as the set of real
numbers. Dedekind is careful to note that this is an assumption that is
incapable of being proven. In modern treatments, Dedekind's assertion is
often taken to be the definition of a line, thereby reducing geometry
to arithmetic. Three-dimensional Euclidean space is defined to be an
affine space whose associated vector space of differences of its
elements is equipped with an inner product.
[4] Also, a three-dimensional
projective space
is now defined non-classically, as the space of all one-dimensional
subspaces (that is, straight lines through the origin) of a
four-dimensional vector space. This shift in foundations requires a new
set of axioms, and if these axioms are adopted, the classical axioms for
geometry become theorems.
According to the famous inaugural lecture given by
Bernhard Riemann in 1854, every mathematical object parametrized by
real numbers may be treated as a point of the
-dimensional space of all such objects.
[2]:140
Contemporary mathematicians follow this idea routinely and find it
extremely suggestive to use the terminology of classical geometry nearly
everywhere.
[2]:138
An object parametrized by
n complex numbers may be treated as a point of a complex
n-dimensional space. However, the same object is also parametrized by 2
n real numbers (if
c is a complex number, then
c =
a +
bi, where
a and
b
are real), thus, a point of a real 2n-dimensional space. The complex
dimension differs from the real dimension. This is only the tip of the
iceberg. The "algebraic" concept of
dimension applies to
vector spaces. For
topological spaces there are several dimension concepts including
inductive dimension and
Hausdorff dimension, which can be non-integer (especially for fractals). Some kinds of spaces (for instance,
measure spaces) admit no concept of dimension at all.
Functions are important mathematical objects. Usually they form infinite-dimensional
function spaces, as noted already by Riemann
[2]:141 and elaborated in the 20th century by
functional analysis.
In order to fully appreciate the generality of this approach one
should note that mathematics is "a pure theory of forms, which has as
its purpose, not the combination of quantities, or of their images, the
numbers, but objects of thought" (
Hermann Hankel, 1867).
[2]:21
This is a controversial characterization of the purpose of mathematics,
which is not necessarily committed to the existence of "objects of
thought".
A space consists now of selected mathematical objects (for instance,
functions on another space, or subspaces of another space, or just
elements of a set) treated as points, and selected relationships between
these points. It shows that spaces are just mathematical structures of
convenience. One may expect that the structures called "spaces" are more
geometric than others, but this is not always true. For example, a
differentiable manifold
(called also smooth manifold) is much more geometric than a measurable
space, but no one calls it "differentiable space" (nor "smooth space").
Taxonomy of spaces
Three taxonomic ranks
Spaces are classified on three levels. Given that each mathematical theory describes its objects by
some of their properties, the first question to ask is: which properties?
For example, the
upper-level classification distinguishes between
Euclidean and
projective spaces,
since the distance between two points is defined in Euclidean spaces
but undefined in projective spaces. These are spaces of different types.
Another example. The question "what is the sum of the three angles of
a triangle" makes sense in a Euclidean space but not in a projective
space; these are spaces of different types. In a non-Euclidean space the
question makes sense but is answered differently, which is not an
upper-level distinction.
Also, the distinction between a Euclidean plane and a Euclidean
3-dimensional space is not an upper-level distinction; the question
"what is the dimension" makes sense in both cases.
In terms of Bourbaki
[5]
the upper-level classification is related to "typical characterization"
(or "typification"). However, it is not the same (since two equivalent
structures may differ in typification).
On the
second level of classification one takes into account
answers to especially important questions (among the questions that make
sense according to the first level). For example, this level
distinguishes between Euclidean and non-Euclidean spaces; between
finite-dimensional and infinite-dimensional spaces; between compact and
non-compact spaces, etc.
In terms of Bourbaki
[5]
the second-level classification is the classification by "species".
Unlike biological taxonomy, a space may belong to several species.
On the
third level of classification, roughly speaking, one takes into account answers to
all possible
questions (that make sense according to the first level). For example,
this level distinguishes between spaces of different dimension, but does
not distinguish between a plane of a three-dimensional Euclidean space,
treated as a two-dimensional Euclidean space, and the set of all pairs
of real numbers, also treated as a two-dimensional Euclidean space.
Likewise it does not distinguish between different Euclidean models of
the same non-Euclidean space.
More formally, the third level classifies spaces up to
isomorphism.
An isomorphism between two spaces is defined as a one-to-one
correspondence between the points of the first space and the points of
the second space, that preserves all relations between the points,
stipulated by the given "typification". Mutually isomorphic spaces are
thought of as copies of a single space. If one of them belongs to a
given species then they all do.
The notion of isomorphism sheds light on the upper-level
classification. Given a one-to-one correspondence between two spaces of
the same type, one may ask whether it is an isomorphism or not. This
question makes no sense for two spaces of different type.
Isomorphisms to itself are called automorphisms. Automorphisms of a
Euclidean space are motions and reflections. Euclidean space is
homogeneous in the sense that every point can be transformed into every
other point by some automorphism.
Two relations between spaces, and a property of spaces
Topological
notions (continuity, convergence, open sets, closed sets etc.) are
defined naturally in every Euclidean space. In other words, every
Euclidean space is also a topological space. Every isomorphism between
two Euclidean spaces is also an isomorphism between the corresponding
topological spaces (called "
homeomorphism"), but the converse is wrong: a homeomorphism may distort distances. In terms of Bourbaki,
[5] "topological space" is an
underlying structure of the "Euclidean space" structure. Similar ideas occur in
category theory:
the category of Euclidean spaces is a concrete category over the
category of topological spaces; the forgetful (or "stripping") functor
maps the former category to the latter category.
A three-dimensional Euclidean space is a special case of a Euclidean space. In terms of Bourbaki,
[5] the species of three-dimensional Euclidean space is
richer
than the species of Euclidean space. Likewise, the species of compact
topological space is richer than the species of topological space.
Euclidean axioms leave no freedom, they determine uniquely all
geometric properties of the space. More exactly: all three-dimensional
Euclidean spaces are isomorphic. In this sense we have "the"
three-dimensional Euclidean space. In terms of Bourbaki, the
corresponding theory is
univalent. In contrast, topological spaces are generally non-isomorphic, their theory is
multivalent.
A similar idea occurs in mathematical logic: a theory is called
categorical if all its models of the same cardinality are isomorphic.
According to Bourbaki,
[6]
the study of multivalent theories is the most striking feature which
distinguishes modern mathematics from classical mathematics.
Types of spaces
Overview of types of abstract spaces. An arrow from space A to space B implies that space A is also a kind of space B. That means, for instance, that a normed vector space is also a metric space.
Linear and topological spaces
Two basic spaces are
linear spaces (also called vector spaces) and
topological spaces.
Linear spaces are of
algebraic nature; there are real linear spaces (over the
field of
real numbers), complex linear spaces (over the field of
complex numbers), and more generally, linear spaces over any field. Every complex linear space is also a real linear space (the latter
underlies the former), since each real number is also a complex number.
[details 1]
Linear operations, given in a linear space by definition, lead to such
notions as straight lines (and planes, and other linear subspaces);
parallel lines; ellipses (and ellipsoids). However, orthogonal
(perpendicular) lines cannot be defined, and circles cannot be singled
out among ellipses. The dimension of a linear space is defined as the
maximal number of
linearly independent
vectors or, equivalently, as the minimal number of vectors that span
the space; it may be finite or infinite. Two linear spaces over the same
field are isomorphic if and only if they are of the same dimension.
Topological spaces are of
analytic nature.
Open sets, given in a topological space by definition, lead to such notions as
continuous functions, paths, maps;
convergent sequences, limits; interior, boundary, exterior. However,
uniform continuity,
bounded sets,
Cauchy sequences,
differentiable functions (paths, maps) remain undefined.
Isomorphisms between topological spaces are traditionally called
homeomorphisms; these are one-to-one correspondences continuous in both directions. The
open interval is homeomorphic to the whole
real line but not homeomorphic to the
closed interval ,
nor to a circle. The surface of a cube is homeomorphic to a sphere (the
surface of a ball) but not homeomorphic to a torus. Euclidean spaces of
different dimensions are not homeomorphic, which seems evident, but is
not easy to prove. Dimension of a topological space is difficult to
define; "inductive dimension" and "Lebesgue covering dimension" are
used. Every subset of a topological space is itself a topological space
(in contrast, only
linear subsets of a linear space are linear spaces). Arbitrary topological spaces, investigated by
general topology (called also
point-set topology) are too diverse for a complete classification (up to homeomorphism). They are inhomogeneous (in general).
Compact topological spaces
are an important class of topological spaces ("species" of this
"type"). Every continuous function is bounded on such space. The closed
interval
and the extended real line
are compact; the open interval
and the line
are not. Geometric topology investigates
manifolds
(another "species" of this "type"); these are topological spaces
locally homeomorphic to Euclidean spaces. Low-dimensional manifolds are
completely classified (up to homeomorphism).
The two structures discussed above (linear and topological) are both
underlying structures of the "linear topological space" structure. That
is, a linear topological space is both a linear (real or complex) space
and a (homogeneous, in fact) topological space. However, an arbitrary
combination of these two structures is generally not a linear
topological space; the two structures must conform, namely, the linear
operations must be continuous.
Every finite-dimensional (real or complex) linear space is a linear
topological space in the sense that it carries one and only one topology
that makes it a linear topological space. The two structures,
"finite-dimensional (real or complex) linear space" and
"finite-dimensional linear topological space", are thus equivalent, that
is, mutually underlying. Accordingly, every invertible linear
transformation of a finite-dimensional linear topological space is a
homeomorphism. In the infinite dimension, however, different topologies
conform to a given linear structure, and invertible linear
transformations are generally not homeomorphisms.
Affine and projective spaces
It is convenient to introduce
affine and
projective spaces by means of linear spaces, as follows. An
-dimensional linear subspace of an
-dimensional linear space, being itself an
-dimensional
linear space, is not homogeneous; it contains a special point, the
origin. Shifting it by a vector external to it, one obtains an
-dimensional affine space. It is homogeneous. In the words of
John Baez,
"an affine space is a vector space that's forgotten its origin". A
straight line in the affine space is, by definition, its intersection
with a two-dimensional linear subspace (plane through the origin) of the
-dimensional linear space. Every linear space is also an affine space.
Every point of the affine space is its intersection with a one-dimensional linear subspace (line through the origin) of the
-dimensional
linear space. However, some one-dimensional subspaces are parallel to
the affine space; in some sense, they intersect it at infinity. The set
of all one-dimensional linear subspaces of an
-dimensional linear space is, by definition, an
-dimensional projective space. Choosing an
-dimensional
affine space as before one observes that the affine space is embedded
as a proper subset into the projective space. However, the projective
space itself is homogeneous. A straight line in the projective space, by
definition, corresponds to a two-dimensional linear subspace of the
-dimensional linear space.
Defined this way, affine and projective spaces are of algebraic
nature; they can be real, complex, and more generally, over any field.
Every real (or complex) affine or projective space is also a
topological space. An affine space is a non-compact manifold; a
projective space is a compact manifold.
Metric and uniform spaces
Distances between points are defined in a
metric space.
Every metric space is also a topological space. Bounded sets and Cauchy
sequences are defined in a metric space (but not just in a topological
space). Isomorphisms between metric spaces are called isometries. A
metric space is called complete if all Cauchy sequences converge. Every
incomplete space is isometrically embedded into its completion. Every
compact metric space is complete; the real line is non-compact but
complete; the open interval
is incomplete.
A topological space is called metrizable, if it underlies a metric space. All manifolds are metrizable.
Every Euclidean space is also a complete metric space. Moreover, all
geometric notions immanent to a Euclidean space can be characterized in
terms of its metric. For example, the straight segment connecting two
given points
and
consists of all points
such that the distance between
and
is equal to the sum of two distances, between
and
and between
and
.
Uniform spaces
do not introduce distances, but still allow one to use uniform
continuity, Cauchy sequences, completeness and completion. Every uniform
space is also a topological space. Every
linear topological
space (metrizable or not) is also a uniform space. More generally, every
commutative topological group is also a uniform space. A
non-commutative topological group, however, carries two uniform
structures, one left-invariant, the other right-invariant. Linear
topological spaces are complete in finite dimension but generally
incomplete in infinite dimension.
Normed, Banach, inner product, and Hilbert spaces
Vectors in a Euclidean space are a linear space, but each vector
has also a length, in other words, norm,
. A (real or complex) linear space endowed with a norm is a
normed space. Every normed space is both a linear topological space and a metric space. A
Banach space is a complete normed space. Many spaces of sequences or functions are infinite-dimensional Banach spaces.
The set of all vectors of norm less than one is called the unit ball
of a normed space. It is a convex, centrally symmetric set, generally
not an ellipsoid; for example, it may be a polygon (on the plane). The
parallelogram law (called also parallelogram identity)
generally fails in normed spaces, but holds for vectors in Euclidean
spaces, which follows from the fact that the squared Euclidean norm of a
vector is its inner product to itself.
An
inner product space
is a (real or complex) linear space endowed with a bilinear (or
sesquilinear) form satisfying some conditions and called inner product.
Every inner product space is also a normed space. A normed space
underlies an inner product space if and only if it satisfies the
parallelogram law, or equivalently, if its unit ball is an ellipsoid.
Angles between vectors are defined in inner product spaces. A
Hilbert space
is defined as a complete inner product space. (Some authors insist that
it must be complex, others admit also real Hilbert spaces.) Many spaces
of sequences or functions are infinite-dimensional Hilbert spaces.
Hilbert spaces are very important for
quantum theory.
[7]
All
-dimensional real inner product spaces are mutually isomorphic. One may say that the
-dimensional Euclidean space is the
-dimensional real inner product space that's forgotten its origin.
Smooth and Riemannian manifolds (spaces)
Smooth manifolds
are not called "spaces", but could be. Smooth (differentiable)
functions, paths, maps, given in a smooth manifold by definition, lead
to tangent spaces. Every smooth manifold is a (topological) manifold.
Smooth surfaces in a finite-dimensional linear space (like the surface
of an ellipsoid, not a polytope) are smooth manifolds. Every smooth
manifold can be embedded into a finite-dimensional linear space. A
smooth path in a smooth manifold has (at every point) the tangent
vector, belonging to the tangent space (attached to this point). Tangent
spaces to an
-dimensional smooth manifold are
-dimensional
linear spaces. A smooth function has (at every point) the differential,
– a linear functional on the tangent space. Real (or complex)
finite-dimensional linear, affine and projective spaces are also smooth
manifolds.
A
Riemannian manifold,
or Riemann space, is a smooth manifold whose tangent spaces are endowed
with inner product (satisfying some conditions). Euclidean spaces are
also Riemann spaces. Smooth surfaces in Euclidean spaces are Riemann
spaces. A hyperbolic non-Euclidean space is also a Riemann space. A
curve in a Riemann space has the length. A Riemann space is both a
smooth manifold and a metric space; the length of the shortest curve is
the distance. The angle between two curves intersecting at a point is
the angle between their tangent lines.
Waiving positivity of inner product on tangent spaces one gets
pseudo-Riemann (especially, Lorentzian) spaces very important for
general relativity.
Measurable, measure, and probability spaces
Waiving distances and angles while retaining volumes (of geometric bodies) one moves toward
measure theory.
Besides the volume, a measure generalizes area, length, mass (or
charge) distribution, and also probability distribution, according to
Andrey Kolmogorov's approach to
probability theory.
A "geometric body" of classical mathematics is much more regular than
just a set of points. The boundary of the body is of zero volume. Thus,
the volume of the body is the volume of its interior, and the interior
can be exhausted by an infinite sequence of cubes. In contrast, the
boundary of an arbitrary set of points can be of non-zero volume (an
example: the set of all rational points inside a given cube). Measure
theory succeeded in extending the notion of volume (or another measure)
to a vast class of sets, so-called
measurable sets.
Indeed, non-measurable sets almost never occur in applications, but
anyway, the theory must restrict itself to measurable sets (and
functions).
Measurable sets, given in a measurable space by definition, lead to
measurable functions and maps. In order to turn a topological space into
a
measurable space one endows it with a
σ-algebra. The σ-algebra of
Borel sets is most popular, but not the only choice (
Baire sets,
universally measurable sets
etc. are used sometimes). Alternatively, a σ-algebra can be generated
by a given collection of sets (or functions) irrespective of any
topology. Quite often, different topologies lead to the same σ-algebra
(for example, the
norm topology and the
weak topology on a
separable Hilbert space). Every subset of a measurable space is itself a measurable space.
Standard measurable spaces (called also standard Borel spaces) are
especially useful. Every Borel set (in particular, every closed set and
every open set) in a Euclidean space (and more generally, in a complete
separable metric space) is a standard measurable space. All uncountable
standard measurable spaces are mutually isomorphic.
A
measure space
is a measurable space endowed with a measure. A Euclidean space with
Lebesgue measure is a measure space. Integration theory defines
integrability and integrals of measurable functions on a measure space.
Sets of measure 0, called null sets, are negligible. Accordingly, a
isomorphism is defined as isomorphism between subsets of full measure (that is, with negligible complement).
A
probability space
is a measure space such that the measure of the whole space is equal to
1. The product of any family (finite or not) of probability spaces is a
probability space. In contrast, for measure spaces in general, only the
product of finitely many spaces is defined. Accordingly, there are many
infinite-dimensional probability measures (especially,
Gaussian measures), but no infinite-dimensional
Lebesgue measure.
Standard probability spaces
are especially useful. Every probability measure on a standard
measurable space leads to a standard probability space. The product of a
sequence (finite or not) of standard probability spaces is a standard
probability space. All non-atomic standard probability spaces are
mutually isomorphic
one of them is the interval
with Lebesgue measure.
These spaces are less geometric. In particular, the idea of
dimension, applicable (in one form or another) to all other spaces, does
not apply to measurable, measure and probability spaces.
A topological space becomes also a measurable space when endowed with the
Borel σ-algebra.
[details 2]
However, the topology is not uniquely determined by its Borel
σ-algebra; and not every σ-algebra is the Borel σ-algebra of some
topology.
[details 3]
Non-commutative geometry
The theoretical study of calculus, known as
mathematical analysis,
led in the early 20th century to the consideration of linear spaces of
real-valued or complex-valued functions. The earliest examples of these
were
function spaces,
each one adapted to its own class of problems. These examples shared
many common features, and these features were soon abstracted into
Hilbert spaces, Banach spaces, and more general
topological vector spaces. These were a powerful toolkit for the solution of a wide range of mathematical problems.
The most detailed information was carried by a class of spaces called
Banach algebras.
These are Banach spaces together with a continuous multiplication
operation. An important early example was the Banach algebra of
essentially bounded measurable functions on a measure space
X.
This set of functions is a Banach space under pointwise addition and
scalar multiplication. With the operation of pointwise multiplication,
it becomes a special type of Banach space, one now called a commutative
von Neumann algebra. Pointwise multiplication determines a representation of this algebra on the Hilbert space of square integrable functions on
X.
An early observation of von Neumann was that this correspondence also
worked in reverse: Given some mild technical hypotheses, a commutative
von Neumann algebra together with a representation on a Hilbert space
determines a measure space, and these two constructions (of a von
Neumann algebra plus a representation and of a measure space) were
mutually inverse.
von Neumann then proposed that non-commutative von Neumann algebras
should have geometric meaning, just as commutative von Neumann algebras
do. Together with
Francis Murray, he produced a classification of von Neumann algebras. The
direct integral construction shows how to break any von Neumann algebra into a collection of simpler algebras called
factors.
von Neumann and Murray classified factors into three types. Type I was
nearly identical to the commutative case. Types II and III exhibited new
phenomena. A type II von Neumann algebra determined a geometry with the
peculiar feature that the dimension could be any non-negative real
number, not just an integer. Type III algebras were those that were
neither types I nor II, and after several decades of effort, these were
proven to be closely related to type II factors.
A slightly different approach to the geometry of function spaces
developed at the same time as von Neumann and Murray's work on the
classification of factors. This approach is the theory of
C*-algebras. Here, the motivating example is the C*-algebra
, where
X is a locally compact Hausdorff topological space. By definition, this is the algebra of continuous complex-valued functions on
X
that vanish at infinity (which loosely means that the farther you go
from a chosen point, the closer the function gets to zero) with the
operations of pointwise addition and multiplication. The
Gelfand–Naimark theorem
implied that there is a correspondence between commutative C*-algebras
and geometric objects: Every commutative C*-algebra is of the form
for some locally compact Hausdorff
X.
The non-commutative C*-algebras, therefore, can be interpreted as
non-commutative spaces, much like non-commutative von Neumann algebras.
Both of these examples are now cases of a field called
non-commutative geometry.
The specific examples of von Neumann algebras and C*-algebras are known
as non-commutative measure theory and non-commutative topology,
respectively. Non-commutative geometry is not merely a pursuit of
generality for its own sake and is not just a curiosity. Non-commutative
spaces arise naturally, even inevitably, from some constructions. For
example, consider the non-periodic
Penrose tilings
of the plane by kites and darts. It is a theorem that, in such a
tiling, every finite patch of kites and darts appears infinitely often.
As a consequence, there is no way to distinguish two Penrose tilings by
looking at a finite portion. This makes it impossible to assign the set
of all tilings a topology in the traditional sense. Despite this, the
Penrose tilings determine a non-commutative C*-algebra, and consequently
they can be studied by the techniques of non-commutative geometry.
Another example, and one of great interest within
differential geometry, comes from
foliations of manifolds. These are ways of splitting the manifold up into smaller-dimensional submanifolds called
leaves,
each of which is locally parallel to others nearby. The set of all
leaves can be made into a topological space. However, the example of an
irrational rotation
shows that this topological space can be bizarre and the techniques of
classical measure theory may be useless on it. However, there is a
non-commutative von Neumann algebra associated to the leaf space of a
foliation, and once again, this gives an otherwise unintelligible space a
good geometric structure.
Schemes
Algebraic geometry studies the geometric properties of
polynomial
equations. Polynomials are a type of function defined by the basic
arithmetic operations of addition and multiplication. Because of this,
they are closely tied to algebra. Algebraic geometry offers a way to
apply geometric techniques to questions of pure algebra, and vice versa.
The type of space that underlies most modern algebraic geometry was introduced by
Alexander Grothendieck and is called a
scheme.
One of the building blocks of a scheme is a topological space.
Topological spaces have continuous functions, but continuous functions
are too general to reflect the underlying algebraic structure of
interest. The other ingredient in a scheme, therefore, is a
sheaf
on the topological space, called the "structure sheaf". On each open
subset of the topological space, the sheaf specifies a collection of
functions, called "regular functions". The topological space and the
structure sheaf together are required to satisfy conditions that mean
the functions come from algebraic operations.
Like manifolds, schemes are defined as spaces which are locally
modeled on a familiar space. In the case of manifolds, the familiar
space is Euclidean space. For a scheme, the local models are called
affine schemes. Affine schemes provide a direct link between algebraic geometry and
commutative algebra. The fundamental objects of study in commutative algebra are
commutative rings. If
R is a commutative ring, then there is a corresponding affine scheme
which translates the algebraic structure of
R
into geometry. Conversely, every affine scheme determines a commutative
ring, the global sections of its structure sheaf. These two operations
are mutually inverse, so affine schemes provide a new language with
which to study questions in commutative algebra. By definition, every
point in a scheme has an open neighborhood which is an affine scheme.
There are many schemes which are not affine. Often, this is an
unavoidable consequence of the geometry of the scheme. The most
important example of this is projective space. Projective space
satisfies a condition called
properness
which is analogous to compactness. Affine schemes cannot be proper
(except in trivial situations like when the scheme has only a single
point), and hence no projective space is an affine scheme (except for
zero-dimensional projective spaces). Projective space is closely related
to the theory of
perspective and to
homogeneous polynomials.
Projective schemes, meaning those that arise as closed subschemes of a
projective space, are the single most important family of schemes.
Several generalizations of schemes have been introduced.
Michael Artin defined an
algebraic space to be an object which is the quotient of a scheme by certain types of
equivalence relations, specifically, equivalence relations which define
étale morphisms. Algebraic spaces retain many of the useful properties of schemes while simultaneously being more flexible. For instance, the
Keel–Mori theorem can be used to show that many
moduli spaces are algebraic spaces.
More general than an algebraic space is a
Deligne–Mumford stack.
DM stacks are similar to schemes, but they permit singularities that
cannot be described solely in terms of polynomials. They play the same
role for schemes that
orbifolds do for
manifolds. For example, the quotient of the affine plane by a finite
group
of rotations around the origin yields a Deligne–Mumford stack that is
not a scheme or an algebraic space. Away from the origin, the quotient
by the group action identifies finite sets of equally spaced points on a
circle. But at the origin, the circle consists of only a single point,
the origin itself, and the group action fixes this point. In the
quotient DM stack, however, this point comes with the extra data of
being a quotient. This kind of refined structure is useful in the theory
of moduli spaces, and in fact, it was originally introduced to describe
moduli of algebraic curves.
A yet further generalization are the
algebraic stacks,
also called Artin stacks. DM stacks are limited to quotients by finite
group actions. While this suffices for many problems in moduli theory,
it is too restrictive for others. Artin stacks permit more general
quotients, and hence more moduli problems can be treated using Artin
stacks than DM stacks.
Topoi
In Grothendieck's work on the
Weil conjectures, he introduced a new type of topology now called a
Grothendieck topology.
A topological space (in the ordinary sense) axiomatizes the notion of
"nearness," making two points be nearby if and only if they lie in many
of the same open sets. By contrast, a Grothendieck topology axiomatizes
the notion of "covering." A covering of a space is a collection of
subspaces that jointly contain all the information of the ambient space.
Since sheaves are defined in terms of coverings, a Grothendieck
topology can also be seen as an axiomatization of the theory of sheaves.
Grothendieck's work on his topologies led him to the theory of
topoi,
which he believed to be one of his greatest mathematical ideas. A sheaf
(either on a topological space or with respect to a Grothendieck
topology) is used to express local data. The
category
of all sheaves carries all possible ways of expressing local data.
Since topological spaces are constructed from points, which are
themselves a kind of local data, the category of sheaves can therefore
be used as a replacement for the original space. Grothendieck
consequently defined a topos to be a category of sheaves and studied
topoi as objects of interest in their own right. These are now called
Grothendieck topoi.
Every topological space determines a topos, and vice versa. There are
topological spaces where taking the associated topos loses information,
but these are generally considered pathological. (A necessary and
sufficient condition is that the topological space be a
sober space.)
Conversely, there are topoi whose associated topological spaces do not
capture the original topos. But, far from being pathological, these
topoi can be of great mathematical interest. For instance,
Grothendieck's theory of
étale cohomology
(which eventually led to the proof of the Weil conjectures) can be
phrased as cohomology in the étale topos of a scheme, and this topos
does not come from a topological space.
Topological spaces in fact lead to very special topoi called
locales. The set of open subsets of a topological space determines a
lattice. The axioms for a topological space cause these lattices to be
complete Heyting algebras.
The theory of locales takes this as its starting point. A locale is
defined to be a complete Heyting algebra, and the elementary properties
of topological spaces are re-expressed and reproved in these terms. The
concept of a locale turns out to be more general than a topological
space, in that every sober topological space determines a unique locale,
but many interesting locales do not come from topological spaces.
Because locales need not have points, the study of locales is somewhat
jokingly called
pointless topology.
Topoi also display deep connections to mathematical logic. Every
Grothendieck topos has a special sheaf called a subobject classifier.
This subobject classifier functions like the set of all possible truth
values. In the topos of sets, the subobject classifier is the set
,
corresponding to "False" and "True". But in other topoi, the subobject
classifier can be much more complicated. Lawvere and Tierney recognized
that axiomatizing the subobject classifier yielded a more general kind
of topos, now known as an
elementary topos, and that elementary topoi were models of
intuitionistic logic.
In addition to providing a powerful way to apply tools from logic to
geometry, this made possible the use of geometric methods in logic.