Posts Tagged ‘linear’
Thursday, December 31st, 2009
How to Write Step Functions
Description
A detailed tutorial on how to write step functions. Step by step tutorial including several examples of how to write step functions for reference.
Overview
A step function, also called a staircase function, is a finite linear combination composed of several different intervals. They are considered to be a piecewise constant function. The graph of a step function is often expressed as steps, or a staircase, which is how it got its name. It simply looks like several disconnected lines, with alternate open and closed ends so that it easily passes the vertical line test for functions.
Tags: closed, combination, constant, diconnected, discrete math, ends, finite, function, graph, intervals, line, linear, lines, open, piecewise, staircase, step, test, vertical
Posted in Discrete Math | No Comments »
Thursday, November 19th, 2009
Overview of Vector Transformations
Description
A detailed tutorial of vector transformations. Step by step tutorial including several examples of vector transformations for reference.
Overview
Vector transformations are not as difficult as one mught think – they are done just like ordinary transformations, except in terms of vectors. Rotation is one of the main types of vector transformations, and is the most common one that is done. In order for a vector to be properly transformed, they must satisfy the orthogonality condition.
Tags: algebra, angle, common, condition, cosine, degrees, linear, orthogonality, properly, ray, rotation, solution, tranformations, vector
Posted in Algebra | No Comments »
Thursday, November 19th, 2009
Defining the Angles Between Vectors
Description
A detailed tutorial on how to define the angles between vectors. Step by step tutorial including several examples of angles between vectors for reference.
Overview
In general, it is easier to find the angle between 2D vectors, rather than 3D vectors. In order to define the angles between vectors, we need to use the dot product in conjunction with a few other functions. The angles between vectors can be expressed as angle = arccos(v1xv2), where v1xv2 is how the dot product is expressed.
Tags: 2D, 3D, absolute, algebra, angle, arccos, conjunction, cosine, define, degrees, dot, function, linear, magnitude, product, radians, value, vector
Posted in Algebra | No Comments »
Thursday, November 19th, 2009
Overview of the Cost Function
Description
A detailed tutorial on the cost function. Step by step tutorial including several examples of the cost function for reference.
Overview
The cost function is a name for a function that is being used in optimization. It is a very important part of an optimization problem. The cost function can be any graph, because all it refers to is the function – the function could be different every time, and it could still be called the cost function. What we learn from this is that the cost function is not unique.
Tags: algebra, constraints, cost, domain, energy, function, functional, graph, linear, maximize, minimize, objective, optimization, solution, unique, variable
Posted in Algebra | No Comments »
Tuesday, November 17th, 2009
Introduction to Orthogonal Vectors
Description
A detailed tutorial on orthogonal vectors. Step by step tutorial including several examples of orthogonal vectors for reference.
Overview
Orthogonal vectors are vectors that are perpendicular. You can determine if vectors are perpendicular by finding the dot product. If the dot product is equal to zero, then the vectors are perpendicular. In certain dimensions, it is possible for three vectors to be perpendicular to each other. In this case, all three of those vectors are considered to be orthogonal. However, in general, orthogonal vectors is a term used to describe a pair of vectors.
Tags: algebra, dot, linear, pair, perpendicular, product, space, three, three-space, two, vectors, zero
Posted in Algebra | No Comments »
Friday, November 6th, 2009
Identity Matrix Explained
Description
A detailed tutorial on the identity matrix. Step by step tutorial including several examples of the identity matrix and how to solve it for reference.
Overview
An indentity matrix is a matrix that is said to be of size n. It is considered to be the n-by-n square matrix with ones on the main diagonal and zeros elsewhere. The identity matrix is denoted as the variable I. The identity matrix has some extremely important properties of its own, especially multiplication properties. It is a unique type of matrix that is found rarely, but is used very often in several different branches of math.
Tags: -1, 0, algebra, diagonal, i, identity, linear, main, matrices, matrix, multiplication, one, properties, square, uniquem, variable, zero
Posted in Algebra | No Comments »
Thursday, November 5th, 2009
Main Diagonal of a Matrix
Description
A detailed tutorial on the main diagonal of a matrix. Step by step tutorial including several examples of main diagonals for reference.
Overview
The main diagonal of a matrix is the diagonal that starts at the top left corner, and continues down and to the right one step until either the other corner is reached (square matrices only), the bottom of the matrix is reached, or the right side of the matrix is reached. The main diagonal is also sometimes called the primary diagonal or the leading diagonal
Tags: algebra, bottom, diagonal, leading, left, linear, main, matrices, matrix, primary, regular, right, square, step, top
Posted in Algebra | No Comments »
Thursday, November 5th, 2009
Linear Subspaces Explained
Description
A detailed tutorial on linear subspaces and how to identify linear subspaces. Step by step tutorial including several examples of linear subspaces for reference.
Overview
A linear subspace is usually referred to as simply a subspace, when it needs to be distinguished from other types of subspaces. Linear subspaces are also sometimes referred to as vector subspaces. In mathematical terms, to identify a linear subspace, we say that K is a field (or a set, like of real numbers), and V is a vector space over K. Elements of V are vectors and elements of K are scalars. W is said to be a subset of V. If W is a vector space itself, with the same vector space operations as V, then it has a subspace of V.
Tags: algebra, element, field, k, linear, number, operations, real, scalar, set, space, subset, subspace, v, vector, W
Posted in Algebra | No Comments »
Thursday, November 5th, 2009
Introduction to Linear Transformations
Description
A detailed tutorial on linear transformations. Step by step tutorial including several examples of linear transformations for reference.
Overview
A linear transformation takes place between two vector spaces. For two vector spaces V and W, there is a map T such that T(v_1 + v_2) = T(v_1) + T(v_2) for any vectors v_1 and v_2 in V, and T(a v) = a T(v) for any scalar a. Examples of linear transformation are often obtained through matrix multiplication. Linear transformations can also be injective or surjective
Tags: algebra, injective, linear, map, matrix, multiplication, scalar, space, surjective, transformation, vector
Posted in Algebra | No Comments »
Tuesday, November 3rd, 2009
Eigenvalues and Eigenvectors Explained
Description
A detailed tutorial on eigenvalues and eigenvectors. Step by step tutorial including several examples of eigenvalues and eigenvectors for reference.
Overview
Eigenvalues and eigenvectors are related concepts commonly used in linear algebra. More specifically, they are properties of a matrix. They give very important information about a matrix, and are used in matrix factorization. Assuming that a matrix is a diagonal matrix (a square matrix or a similar matrix that you can calculate diagonals on), then the eigenvalues are the numbers on the diagonal and the eigenvectors are the basis vectors to which there numbers refer. You cannot have an eigenvector without an eigenvalue. However, you can have an eigenvalue without an eigenvector.
Tags: algebra, basis, diagonal, eigenvalue, eigenvector, factorization, linear, matrices, matrix, properties, square, transformations, vectors
Posted in Algebra | No Comments »
