Vector space dimensionality

The matrix A in Eq. (7-21) is comprised of orthogonal vectors. Orthogonal vectors have a dot product of zero. The mutually perpendicular (and independent) Cartesian coordinates of 3-space are orthogonal. An orthogonal n x n such as matr ix A may be thought of as n columns of n-element vectors that are mutually perpendicular in an n-dimensional vector space. 

The ordered set of measurements made on each sample is called a data vector. The group of data vectors, identically ordered, for all of the samples is called the data matrix. If the data matrix is arranged such that successive rows of the matrix correspond to the different samples, then the columns correspond to the variables as in Figure 1. Each variable, or aspect of the sample that is measured, defines an axis in space the samples thus possess a data stmcture when plotted as points in that / -dimensional vector space, where n is the number of variables. 

Rules of matrix algebra can be appHed to the manipulation and interpretation of data in this type of matrix format. One of the most basic operations that can be performed is to plot the samples in variable-by-variable plots. When the number of variables is as small as two then it is a simple and familiar matter to constmct and analyze the plot. But if the number of variables exceeds two or three, it is obviously impractical to try to interpret the data using simple bivariate plots. Pattern recognition provides computer tools far superior to bivariate plots for understanding the data stmcture in the //-dimensional vector space. 

In terms of linear vector space, Buckingham s theorem (Theorem 2) simply states that the null space of the dimensional matrix has a fixed dimension, and Van Driest s rule (Theorem 3) then specifies the nullity of the dimensional matrix. The problem of finding a complete set of B-numbers is equivalent to that of computing a fundamental system of solutions of equation 13 called a complete set of B-vectors. For simplicity, the matrix formed by a complete set of B-vectors will be called a complete B-matrix. It can also be demonstrated that the choice of reference dimensions does not affect the B-numbers (22). 

In elementary algebra, a linear function of the coordinates xi of a variable vector f = (jci, JT2,..., Jc ) of the finite-dimensional vector space V = V P) is a polynomial function of the special form... 

Formally, to each site H is assigned a A -dimensional complex color vector space spanned by the color eigenvectors >, >, so that a... 

It is possible to perform a systematic decoupling of this moment expansion using the superoperator formalism (34,35). An infinite dimensional operator vector space defined by a basis of field operators Xj which supports the scalar product (or metric)... 

It is worth noting here that in a finite-dimensional space any linear operator is bounded. All of the linear bounded operators from X into Y constitute what is called a normed vector space, since the norm j4 of an operator A satisfies all of the axioms of the norm ... 

Linear operators in finite-dimensional spaces. It is supposed that an n-dimensional vector space R is equipped with an inner product (, ) and associated norm a = / x x). By the definition of finite-dimensional space, any vector x G i n can uniquely be represented as a linear combina-... 

A set of n vectors of dimension n which are linearly independent is called a basis of an -dimensional vector space. There can be several bases of the same vector... 

The set of unit vectors of dimension n defines an n-dimensional rectangular (or Cartesian) coordinate space 5 . Such a coordinate space S" can be thought of as being constructed from n base vectors of unit length which originate from a common point and which are mutually perpendicular. Hence, a coordinate space is a vector space which is used as a reference frame for representing other vector spaces. It is not uncommon that the dimension of a coordinate space (i.e. the number of mutually perpendicular base vectors of unit length) exceeds the dimension of the vector space that is embedded in it. In that case the latter is said to be a subspace of the former. For example, the basis of 5 is ... 

A set of complete orthonormal functions ipfx) of a single variable x may be regarded as the basis vectors of a linear vector space of either finite or infinite dimensions, depending on whether the complete set contains a finite or infinite number of members. The situation is analogous to three-dimensional cartesian space formed by three orthogonal unit vectors. In quantum mechanics we usually (see Section 7.2 for an exception) encounter complete sets with an infinite number of members and, therefore, are usually concerned with linear vector spaces of infinite dimensionality. Such a linear vector space is called a Hilbert space. The functions ffx) used as the basis vectors may constitute a discrete set or a continuous set. While a vector space composed of a discrete set of basis vectors is easier to visualize (even if the space is of infinite dimensionality) than one composed of a continuous set, there is no mathematical reason to exclude continuous basis vectors from the concept of Hilbert space. In Dirac notation, the basis vectors in Hilbert space are called ket vectors or just kets and are represented by the symbol tpi) or sometimes simply by /). These ket vectors determine a ket space. 

In the vector space L defined over the field of real numbers, every operator acting on L does not necessarily have eigenvalues and eigenvectors. Thus for the operation of 7t/2 rotation on a two-dimensional vector space of (real) position vectors, the operator has no eigenvectors since there is no non-zero vector in this space which transforms into a real multiple of itself. However, if L is a vector space over the field of complex numbers, every operator on L has eigenvectors. The number of eigenvalues is equal to the dimension of the space L. The set of eigenvalues of an operator is called its spectrum. 

Each irreducible representation of a group consists of a set of square matrices of order lt. The set of matrix elements with the same index, grouped together, one from each matrix in the set, constitutes a vector in -dimensional space. The great orthogonality theorem (16) states that all these vectors are mutually orthogonal and that each of them is normalized so that the square of its length is equal to g/li. This interpretation becomes more obvious when (16) is unpacked into separate expressions ... 

A matrix of order l has l2 elements. Each irreducible representation T, must therefore contribute If -dimensional vectors. The orthogonality theorem requires that the total set of Y f vectors must be mutually orthogonal. Since there can be no more than g orthogonal vectors in -dimensional space, the sum Y i cannot exceed g. For a complete set (19) is implied. Since the character of an identity matrix always equals the order of the representation it further follows that... 

The set of all orthogonal transformations in a three-dimensional real vector space (i.e. a space defined over the field of real numbers) constitutes a group denoted by 0/(3). Alternatively it may be defined as the group of all 3 x 3 orthogonal matrices. The two groups are isomorphic. 

It should be clear that the set of all real orthogonal matrices of order n with determinants +1 constitutes a group. This group is denoted by 0(n) and is a continuous, connected, compact, n(n — l)/2 parameter9 Lie group. It can be thought of as the set of all proper rotations in a real n-dimensional vector space. If xux2,. ..,xn are the orthonormal basis vectors in this space, a transformation of 0(n) leaves the quadratic form =1 x invariant. 

Let u and r be a pair of vectors in a two-dimensional vector space defined over the field of complex numbers. A rotation in this space transforms u and... 

This linear combination is clearly different from (3). The implication is that the two-dimensional vector space needed to describe the spin states of silver atoms must be a complex vector space an arbitrary vector in this space is written as a linear combination of the base vectors sf with, in general complex coefficients. This is the first example of the fundamental property of quantum-mechanical states to be represented only in an abstract complex vector space [55]. 

Together these unit vectors define an n-dimensional vector space and they are said to constitute an orthonormal set. State vectors of this type are... 

This algebra has a representation on the exterior algebra F = / of an infinite dimensional vector space V = Cdpi 0 Cdp2 defined by... 

Principal component analysis is a simple vector space transform, allowing the dimensionality of a data set to be reduced, while at the same time minimizing... 

Any 37/-dimensional Cartesian vector that is associated with a point on the constraint surface may be divided into a soft component, which is locally tangent to the constraint surface and a hard component, which is perpendicular to this surface. The soft subspace is the /-dimensional vector space that contains aU 3N dimensional Cartesian vectors that are locally tangent to the constraint surface. It is spanned by / covariant tangent basis vectors... 

The mathematics we shall need is confined to the properties of vector spaces in which the scalar values are real numbers. From a mathematical viewpoint the whole discussion will take place in the context of two vector spaces, an S-dimensional space of chemical mechanisms and a Q-dimen-sional space of chemical reactions, which are related to each other by the fact that each mechanism m is associated with a unique reaction R(m) which it produces. The function R is a transformation of mechanisms to reactions which is linear by virtue of the fact that reactions are additive in a chemical system and that the reaction associated with combined mechanisms mt + m2 is R(m,) + R(m2). All mechanisms are combinations of a simplest kind of mechanism, called a step, which ideally consists of a one-step molecular interaction. Each step produces one of the elementary reactions which form a basis for the space of all reactions. 

For simplicity we speak of a mechanism or a reaction, rather than a mechanism vector or reaction vector. The distinction lies in the fact that a reaction r (or mechanism) is essentially the same whether its rate of advancement is p or a, whereas pr and or are different vectors (for p a). Therefore, a reaction could properly be defined as a one-dimensional vector space which contains all the scalar multiples of a single reaction vector, but the mathematical development is simpler if a reaction is defined as a vector. This leaves open the question of when two reactions, or two mechanisms, are essentially different from a chemical viewpoint, which will be taken up... 

The elementary reactions in Eqs. (1) are not necessarily linearly independent, and, accordingly, let Q denote the maximum number of them in a linearly independent subset. This means that the set of all linear combinations of them defines a 0-dimensional vector space, called the reaction space. In matrix language 0 is the rank of the S x A matrix (2) of stoichiometric coefficients which appear in the elementary reactions (1) ... 

R3, then sa and a +aj are also 3-dimensional vectors, and the vector space is closed under multiplication by scalars, and addition. This is the fundamental property of any linear vector space. Consider the vectors a and... 

Exercise 1.5 (Geometry of multiplication in C) 77zc complex plane can be considered as a two-dimensional real vector space, with basis 1, i. Show that multiplication by any complex number c is a linear transformation. Find the matrix for multiplication by i in the given basis. Find the matrix for multiplication by e, where d is a real number. Find the matrix for multiplication by a + ib. where a and b are real numbers. 

Definition 2.3 A complex vector space is finite-dimensional if it has a finite basis. Any complex vector space that is not finite-dimensional is infinitedimensional. 

Suppose that V is a finite-dimensional complex vector space. By the definition this means that V has a finite basis. It turns out that all the different bases of V must be the same size. This is geometrically plausible for real Euclidean vector spaces, where one can visualize a basis of size one determiiung a line, a basis of size two determining a plane, and so on. The same is true for complex vector spaces. A key part of the proof, useful in its own right, is the following fact. 

Proposition 2.1 Suppose V is a finite-dimensional vector space with basis vi,. .., u . Suppose ,..., Urn is a linearly independent subset of V. Then m < n. 

Proposition 2.2 Let V be a finite-dimensional complex vector space. Suppose ui,. .., u and mi, are both bases ofV. Then n = m. 

---