Vectors

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Basic Vector Math

Basic Vector Operations

Both a magnitude and a direction must be specified for a vector quantity, in contrast to a scalar quantity which can be quantified with just a number. Any number of vector quantities of the same type (i.e., same units) can be combined by basic vector operations.

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Math of vectors

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Graphical Vector Addition

Adding two vectors A and B graphically can be visualized like two successive walks, with the vector sum being the vector distance from the beginning to the end point. Representing the vectors by arrows drawn to scale, the beginning of vector B is placed at the end of vector A. The vector sum R can be drawn as the vector from the beginning to the end point.

The process can be done mathematically by finding the components of A and B, combining to form the components of R, and then converting to polar form.

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Example of Vector Components

Finding the components of vectors for vector addition involves forming a right triangle from each vector and using the standard triangle trigonometry.

The vector sum can be found by combining these components and converting to polar form.

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Polar Form Example

After finding the components for the vectors A and B, and combining them to find the components of the resultant vector R, the result can be put in polar form by

Some caution should be exercised in evaluating the angle with a calculator because of ambiguities in the arctangent on calculators.

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Vector concepts

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Combining Vector Components

After finding the components for the vectors A and B, these components may be just simply added to find the components of the resultant vector R.

The components fully specify the resultant of the vector addition, but it is often desirable to put the resultant in polar form.

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Resolving a Vector Into Components

Vectors are resolved into components by use of the triangle trig relationships. You may change the length or angle of the polar form of the vector, and the components will be calculated below.

Note: this Javascript routine does not work for angle exactly equal to 90° .

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Magnitude and Direction from Components

If the components of a vector are known, then its magnitude and direction can be calculated with the use of the Pythagorean relationship and triangle trig. This is called the polar form of the vector.

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Vector Addition, Two Vectors

Vector addition involves finding vector components, adding them and finding the polar form of the resultant.

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Vector concepts

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Vector Addition, Three Vectors

Vector addition involves finding vector components, adding them and finding the polar form of the resultant.

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Vector concepts

HyperPhysics***** Mechanics

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Vector Addition, Four Vectors

Vector addition involves finding vector components, adding them and finding the polar form of the resultant.

Index

Vector concepts

HyperPhysics***** Mechanics

R Nave

Coordinate Systems

Cartesian coordinate system

Main article: Cartesian coordinate system

The Cartesian coordinate system in the plane.

The prototypical example of a coordinate system is the Cartesian coordinate system. In the plane, two perpendicular lines are chosen and the coordinates of a point are taken to be the signed distances to the lines.

Rectangular coordinates.svg

In three dimensions, three perpendicular planes are chosen and the three coordinates of a point are the signed distances to each of the planes. This can be generalized to create n coordinates for any point in n-dimensional Euclidean space.

Polar coordinate system

Main article: Polar coordinate system

The Polar coordinate system in the plane.

Vectors as Matrices

It is also sometimes useful to use a Matrix when dealing with Vector Multiplication.

Matrix notation

The definition of the cross product can also be represented by the determinant of a formal matrix:

$\mathbf{a}\times\mathbf{b}= \begin{vmatrix} \mathbf{i} & \mathbf{j} & \mathbf{k} \\ a_1 & a_2 & a_3 \\ b_1 & b_2 & b_3 \\ \end{vmatrix}.$

This determinant can be computed using Sarrus' rule or Cofactor expansion.

Using Sarrus' Rule, it expands to

$\mathbf{a}\times\mathbf{b}= \mathbf{i}a_2b_3 + \mathbf{j}a_3b_1 + \mathbf{k}a_1b_2 - \mathbf{i}a_3b_2 - \mathbf{j}a_1b_3 - \mathbf{k}a_2b_1.$

Using Cofactor expansion along the first row instead, it expands to^[4]

$\mathbf{a}\times\mathbf{b}= \begin{vmatrix} a_2 & a_3\\ b_2 & b_3 \end{vmatrix} \mathbf{i} - \begin{vmatrix} a_1 & a_3\\ b_1 & b_3 \end{vmatrix} \mathbf{j}+ \begin{vmatrix} a_1 & a_2\\ b_1 & b_2 \end{vmatrix} \mathbf{k}$

which gives the components of the resulting vector directly.

Geometric meaning

See also: Triple product

Figure 1: The area of a parallelogram as a cross product

Figure 2: The volume of a parallelepiped using dot and cross-products; dashed lines show the projections of c onto a × b and of a onto b × c, a first step in finding dot-products.

The magnitude of the cross product can be interpreted as the positive area of the parallelogram having a and b as sides (see Figure 1):

$A = | \mathbf{a} \times \mathbf{b}| = | \mathbf{a} | | \mathbf{b}| \sin \theta. \,\!$

Indeed, one can also compute the volume V of a parallelepiped having a, b and c as sides by using a combination of a cross product and a dot product, called scalar triple product (see Figure 2):

$V = |\mathbf{a} \cdot (\mathbf{b} \times \mathbf{c})|.$

Figure 2 demonstrates that this volume can be found in two ways, showing geometrically that the identity holds that a "dot" and a "cross" can be interchanged without changing the result. That is:

$V =\mathbf{(a \times b) \cdot c} = \mathbf{a \cdot (b \times c)} \ .$

Because the magnitude of the cross product goes by the sine of the angle between its arguments, the cross product can be thought of as a measure of "perpendicularness" in the same way that the dot product is a measure of "parallelness". Given two unit vectors, their cross product has a magnitude of 1 if the two are perpendicular and a magnitude of zero if the two are parallel.

Determinants

In algebra, the determinant is a special number associated with any square matrix. The fundamental geometric meaning of a determinant is a scale factor or coefficient for measure when the matrix is regarded as a linear transformation. Thus a 2 × 2 matrix with determinant 2 when applied to a set of points with finite area will transform those points into a set with twice the area. Determinants are important both in calculus, where they enter the substitution rule for several variables, and in multilinear algebra.

When its scalars are taken from a field F, a matrix is invertible if and only if its determinant is nonzero; more generally, when the scalars are taken from a commutative ring R, the matrix is invertible if and only if its determinant is a unit of R. Determinants are not that well-behaved for noncommutative rings.

The determinant of a matrix A is denoted det(A), or without parentheses: det A. An alternative notation, used for compactness, especially in the case where the matrix entries are written out in full, is to denote the determinant of a matrix by surrounding the matrix entries by vertical bars instead of the usual brackets or parentheses. Thus

$\begin{vmatrix} a & b & c\\d & e & f\\g & h & i \end{vmatrix}\$ denotes the determinant of the matrix $\begin{bmatrix}a&b&c\\ d&e&f\\g&h&i\end{bmatrix}.$

For a fixed nonnegative integer n, there is a unique determinant function for the n×n matrices over any commutative ring R. In particular, this unique function exists when R is the field of real or complex numbers.

Interpretation as the area of a parallelogram

The area of the parallelogram is the absolute value of the determinant of the matrix formed by the vectors representing the parallelogram's sides.

The 2×2 matrix

$A = \begin{bmatrix} a & b\\c & d \end{bmatrix}\,$

has determinant

$\det A = ad - bc.\$

If A is a 2x2 matrix, its determinant det A can be viewed as the oriented area of the parallelogram with vertices at (0,0), (a,b), (a + c, b + d), and (c,d). The oriented area is the same as the usual area, except that it is negative when the vertices are listed in clockwise order.

Further, the parallelogram itself can be viewed as the unit square transformed by the matrix A. The assumption here is that a linear transformation is applied to row vectors as the vector-matrix product $x T A T$ , where $x$ is a column vector. The parallelogram in the figure is obtained by multiplying matrix A (which stores the co-ordinates of our parallelogram) with each of the row vectors $\begin{bmatrix} 0 & 0 \end{bmatrix}, \begin{bmatrix} 1 & 0 \end{bmatrix}, \begin{bmatrix} 1 & 1 \end{bmatrix}$ and $\begin{bmatrix}0 & 1\end{bmatrix}$ in turn. These row vectors define the vertices of the unit square. With the more common matrix-vector product $A x,$ the parallelogram has vertices at $\begin{bmatrix} 0 \\ 0 \end{bmatrix}, \begin{bmatrix} a \\ c \end{bmatrix}, \begin{bmatrix} a+b \\ c+d \end{bmatrix}$ and $\begin{bmatrix} b \\ d \end{bmatrix}$ (note that $A x = (x T A T) T$ ).

Thus when the determinant is equal to one, then the matrix represents an equi-areal mapping.

3-by-3 matrices

The volume of this Parallelepiped is the absolute value of the determinant of the matrix formed by the rows r1, r2, and r3.

The determinant of a 3×3 matrix

$A=\begin{bmatrix}a&b&c\\ d&e&f\\g&h&i\end{bmatrix}$

is given by

$\det A = aei + bfg + cdh - afh - bdi - ceg.\$

The determinant of a 3x3 matrix can be calculated by its diagonals.

The rule of Sarrus is a mnemonic for this formula: the sum of the products of three diagonal north-west to south-east lines of matrix elements, minus the sum of the products of three diagonal south-west to north-east lines of elements when the copies of the first two columns of the matrix are written beside it as below:

$\begin{matrix} \color{red}a & \color{red}b & \color{red}c & a & b \\ d & \color{red}e & \color{red}f & \color{red}d & e \\ g & h & \color{red}i & \color{red}g & \color{red}h \end{matrix} \quad - \quad \begin{matrix} a & b & \color{blue}c & \color{blue}a & \color{blue}b \\ d & \color{blue}e & \color{blue}f & \color{blue}d & e \\ \color{blue}g & \color{blue}h & \color{blue}i & g & h \end{matrix}$

This mnemonic does not carry over into higher dimensions.