Lec 31: Inner products. An inner product on a vector space V
... positive definite. Thus, positive definite matrices correspond to inner products in Rn . Now look at other examples of inner product spaces (i. e. vector spaces with an inner product) V . Finite dimensional V with inner product are called Euclidean spaces. Next is an example of infinite-dimensional ...
... positive definite. Thus, positive definite matrices correspond to inner products in Rn . Now look at other examples of inner product spaces (i. e. vector spaces with an inner product) V . Finite dimensional V with inner product are called Euclidean spaces. Next is an example of infinite-dimensional ...
A blitzkrieg through decompositions of linear transformations
... If W is an invariant subspace for the linear transformation of T on the vector space V and v ∈ V , then the T -conductor of v into W is the set of all polynomials g in the corresponding field such that g(T )v ∈ W . As for annihilators, the set of conductors also form an ideal and it has an unique mo ...
... If W is an invariant subspace for the linear transformation of T on the vector space V and v ∈ V , then the T -conductor of v into W is the set of all polynomials g in the corresponding field such that g(T )v ∈ W . As for annihilators, the set of conductors also form an ideal and it has an unique mo ...
On Certain Type of Modular Sequence Spaces
... where p, known as the kernel of M , is right continuous for t > 0, p(0) = 0, p(t) > 0 for t > 0, p is non- decreasing and p(x) → ∞ as x → ∞. Given an Orlicz function M with kernel p, define q(s) =sup{t : p(t) ≤ s}, s ≥ 0. Then x q possesses the same properties as p and the function N defined as N (x) ...
... where p, known as the kernel of M , is right continuous for t > 0, p(0) = 0, p(t) > 0 for t > 0, p is non- decreasing and p(x) → ∞ as x → ∞. Given an Orlicz function M with kernel p, define q(s) =sup{t : p(t) ≤ s}, s ≥ 0. Then x q possesses the same properties as p and the function N defined as N (x) ...
A write-up on the combinatorics of the general linear group
... 3.4. The matrix of intersection numbers. We earlier saw a description of the matrix of intersection numbers of two arbitrary flags. We now use this to define a map from the collection of flags of a given type, to permutations of that type. • Construct the matrix of intersection numbers of the standa ...
... 3.4. The matrix of intersection numbers. We earlier saw a description of the matrix of intersection numbers of two arbitrary flags. We now use this to define a map from the collection of flags of a given type, to permutations of that type. • Construct the matrix of intersection numbers of the standa ...
Linear Algebra Libraries: BLAS, LAPACK - svmoore
... • A LAPACK subroutine name is in the form pmmaaa, where: – p is a one-letter code denoting the type of numerical constants used. S, D stand for real floating point arithmetic respectively in single and double precision, while C and Z stand for complex arithmetic with respectively single and double p ...
... • A LAPACK subroutine name is in the form pmmaaa, where: – p is a one-letter code denoting the type of numerical constants used. S, D stand for real floating point arithmetic respectively in single and double precision, while C and Z stand for complex arithmetic with respectively single and double p ...
Sample Final Exam
... S = p(x) ∈ P3 p(2) − p(1) = 0 Find a basis for this subspace. Answer: Suppose that p(x) = ax2 + bx + c is a polynomial in S. Then, p(2) = 4a + 2b + c and p(1) = a + b + c, so that p(2) − p(1) = 3a + b. Thus, 3a + b = 0, so b = −3a. Thus, we can write p(x) as p(x) = ax2 − 3ax + c = a(x2 − 3x) + c Th ...
... S = p(x) ∈ P3 p(2) − p(1) = 0 Find a basis for this subspace. Answer: Suppose that p(x) = ax2 + bx + c is a polynomial in S. Then, p(2) = 4a + 2b + c and p(1) = a + b + c, so that p(2) − p(1) = 3a + b. Thus, 3a + b = 0, so b = −3a. Thus, we can write p(x) as p(x) = ax2 − 3ax + c = a(x2 − 3x) + c Th ...
Vector space
A vector space (also called a linear space) is a collection of objects called vectors, which may be added together and multiplied (""scaled"") by numbers, called scalars in this context. Scalars are often taken to be real numbers, but there are also vector spaces with scalar multiplication by complex numbers, rational numbers, or generally any field. The operations of vector addition and scalar multiplication must satisfy certain requirements, called axioms, listed below. Euclidean vectors are an example of a vector space. They represent physical quantities such as forces: any two forces (of the same type) can be added to yield a third, and the multiplication of a force vector by a real multiplier is another force vector. In the same vein, but in a more geometric sense, vectors representing displacements in the plane or in three-dimensional space also form vector spaces. Vectors in vector spaces do not necessarily have to be arrow-like objects as they appear in the mentioned examples: vectors are regarded as abstract mathematical objects with particular properties, which in some cases can be visualized as arrows.Vector spaces are the subject of linear algebra and are well understood from this point of view since vector spaces are characterized by their dimension, which, roughly speaking, specifies the number of independent directions in the space. A vector space may be endowed with additional structure, such as a norm or inner product. Such spaces arise naturally in mathematical analysis, mainly in the guise of infinite-dimensional function spaces whose vectors are functions. Analytical problems call for the ability to decide whether a sequence of vectors converges to a given vector. This is accomplished by considering vector spaces with additional structure, mostly spaces endowed with a suitable topology, thus allowing the consideration of proximity and continuity issues. These topological vector spaces, in particular Banach spaces and Hilbert spaces, have a richer theory.Historically, the first ideas leading to vector spaces can be traced back as far as the 17th century's analytic geometry, matrices, systems of linear equations, and Euclidean vectors. The modern, more abstract treatment, first formulated by Giuseppe Peano in 1888, encompasses more general objects than Euclidean space, but much of the theory can be seen as an extension of classical geometric ideas like lines, planes and their higher-dimensional analogs.Today, vector spaces are applied throughout mathematics, science and engineering. They are the appropriate linear-algebraic notion to deal with systems of linear equations; offer a framework for Fourier expansion, which is employed in image compression routines; or provide an environment that can be used for solution techniques for partial differential equations. Furthermore, vector spaces furnish an abstract, coordinate-free way of dealing with geometrical and physical objects such as tensors. This in turn allows the examination of local properties of manifolds by linearization techniques. Vector spaces may be generalized in several ways, leading to more advanced notions in geometry and abstract algebra.