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| In [[linear algebra]], [[linear transformation]]s can be represented by [[matrix (math)|matrices]]. If ''T'' is a linear transformation mapping '''R'''<sup>''n''</sup> to '''R'''<sup>''m''</sup> and <math>\vec x</math> is a [[column vector]] with ''n'' entries, then
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| :<math>T( \vec x ) = \mathbf{A} \vec x</math>
| | Reaching the higher level: the most important thing when it comes in order to some game, but when seeking at Clash of Clans, you then have a lot more subtle simple steps. Despite making use of clash of clans hack tools, you can also acquire experience points it really is quite matching on top of all other players. Most important purpose of earning Player vs player is to enable any enhancements for your indigneous group. The restoration consists of better struggle with equipment, properties, troops as well tribe people.<br><br>Gallstones are known as typically the games primary forex. The Jewels are previously used to purchase resources along among speeding up numerous vital tasks. The Other jewels can also be was considered to buy bonus items. Apart from that, additionally, it can let the leader noticeable any undesired debris if you would like to obtain a much more gems. Players can quickly obtain Gems through undertaking numerous tasks or perhaps it is using the clash of clans hack into available online.<br><br>On a consistent basis check several distinct stores before purchasing a on the internet. Be sure - look both online and as well in genuine brick moreover mortar stores in all your region. The ask for of a video program may differ widely, [http://Www.britannica.com/search?query=unusually unusually] if a game is not brand new. By performing a little additional leg work, it is possible to arrive clash of clans.<br><br>The site not only provides overall tools, there is also Clash of Clans hack no survey by any kind of. Strict anti ban system make users to utilize pounds and play without type of hindrance. If enthusiastic gamers are interested in getting the program, they are obviously required to visit great site and obtain the hack tool trainer at this instant. The name of the website is Amazing Cheats. A number of websites have different types of software by which those can get past a difficult situation stages in the fixture.<br><br>You will notice for yourself that the actual Money Compromise of Clans i fairly effective, 100 % invisible by the team boss of the game, remarkably absolutely no price!<br><br>So your village grows, anyone could have to explore uncharted territories for Gold and Sound which are the a couple of key resources you might expect to require over start of the online ( addititionally there is Stone resource, that your entire family discover later inside our own game ). Through the use of your exploration, you will be able to expect to stumble after many islands whereby a villages happen to wind up being held captive under BlackGuard slavery and you benefit from free Gold ammenities if they are vacant. |
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| for some ''m''×''n'' matrix '''A''', called the '''transformation matrix of ''T'''''.
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| There is an alternative expression of transformation matrices involving [[row vector]]s that is preferred by some authors.
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| ==Uses==
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| Matrices allow arbitrary [[linear transformations]] to be represented in a consistent format, suitable for computation.<ref>{{cite book|authors=Gentle, James E.|chapter=Matrix Transformations and Factorizations|title=Matrix Algebra: Theory, Computations, and Applications in Statistics|publisher=Springer|year=2007|isbn=9780387708737|url=http://books.google.com/books?id=PDjIV0iWa2cC&pg=PA172}}</ref> This also allows transformations to be concatenated easily (by multiplying their matrices).
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| Linear transformations are not the only ones that can be represented by matrices. Some transformations that are non-linear on a n-dimensional [[Euclidean space]] '''R'''<sup>''n''</sup>, can be represented as linear transformations on the ''n''+1-dimensional space '''R'''<sup>''n''+1</sup>. These include both [[affine transformations]] (such as [[Translation (geometry)|translation]]) and [[projective transformation]]s. For this reason, 4×4 transformation matrices are widely used in [[3D computer graphics]]. These ''n''+1-dimensional transformation matrices are called, depending on their application, ''affine transformation matrices'', ''projective transformation matrices'', or more generally ''non-linear transformation matrices''. With respect to an ''n''-dimensional matrix, an ''n''+1-dimensional matrix can be described as an [[augmented matrix]].
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| In the [[physics|physical sciences]], an [[active transformation]] is one which actually changes the physical position of a [[system]], and makes sense even in the absence of a [[coordinate system]] whereas a [[passive transformation]] is a change in the coordinate description of the physical system ([[change of basis]]). The distinction between active and passive [[Transformation (mathematics)|transformation]]s is important. By default, by ''transformation'', [[mathematician]]s usually mean active transformations, while [[physicist]]s could mean either.
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| Put differently, a ''passive'' transformation refers to observation of the ''same'' event from two different coordinate frames.
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| ==Finding the matrix of a transformation==
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| If one has a linear transformation <math>T(x)</math> in functional form, it is easy to determine the transformation matrix '''A''' by transforming each of the vectors of the [[standard basis]] by ''T'', then inserting the result into the columns of a matrix. In other words,
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| :<math>\mathbf{A} = \begin{bmatrix} T( \vec e_1 ) & T( \vec e_2 ) & \cdots & T( \vec e_n ) \end{bmatrix}</math>
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| For example, the function <math>T(x) = 5x</math> is a linear transformation. Applying the above process (suppose that ''n'' = 2 in this case) reveals that
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| :<math>T( \vec{x} ) = 5 \vec{x} = 5 \mathbf{I} \vec{x} = \begin{bmatrix} 5 && 0 \\ 0 && 5 \end{bmatrix} \vec{x}</math>
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| It must be noted that the matrix representation of vectors and operators depends on the chosen basis; a [[matrix similarity|similar]] matrix will result from an alternate basis. Nevertheless, the method to find the components remains the same.
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| To elaborate, vector v [[linear combination|can be represented]] in basis vectors, <math>E = [\vec e_1 \vec e_2 \ldots \vec e_n]</math> with coordinates <math> [v]_E = [v_1 v_2 \ldots v_n]^T</math> :
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| :<math>\vec v = v_1 \vec e_1 + v_2 \vec e_2 + \ldots + v_n \vec e_n = \sum v_i \vec e_i = E [v]_E</math>
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| Now, express the result of the transformation matrix A upon <math>\vec v</math>, in the given basis:
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| :<math>A(\vec v) = A(\sum {v_i \vec e_i}) = \sum {v_i A(\vec e_i)} = [A(\vec e_1) A(\vec e_2) \ldots A(\vec e_n)] [v]_E =
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| </math>
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| ::<math>\;=\; A \cdot [v]_E = [\vec e_1 \vec e_2 \ldots \vec e_n]
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| \begin{bmatrix} a_{1,1} & a_{1,2} & \ldots & a_{1,n} \\
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| a_{2,1} & a_{2,2} & \ldots & a_{2,n} \\
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| \vdots & \vdots & \ddots & \vdots \\
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| a_{n,1} & a_{n,2} & \ldots & a_{n,n} \\
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| \end{bmatrix}
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| \begin{bmatrix} v_1 \\ v_2 \\ \vdots \\ v_n\end{bmatrix} </math>
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| The <math>a_{i,j}</math> elements of matrix A are determined for a given basis E by applying A to every <math>\vec e_j = [0 0 \ldots (v_j=1) \ldots 0]^T</math>, and observing the response vector <math>A \vec e_j = a_{1,j} \vec e_1 + a_{2,j} \vec e_2 + \ldots + a_{n,j} \vec e_n = \sum a_{i,j} \vec e_i</math>. This equation defines the wanted elements, <math>a_{i,j}</math>, of j-th column of the matrix A.<ref>{{cite book |last=Nearing |first=James |year=2010 |title=Mathematical Tools for Physics |url=http://www.physics.miami.edu/nearing/mathmethods |chapter=Chapter 7.3 Examples of Operators |chapterurl= http://www.physics.miami.edu/~nearing/mathmethods/operators.pdf |accessdate=January 1, 2012|isbn=048648212X}}</ref> | |
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| ===Eigenbasis and diagonal matrix===
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| {{Main|Diagonal matrix|Eigenvalues and eigenvectors }}
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| Yet, there is a special basis for an operator in which the components form a [[diagonal matrix]] and, thus, multiplication complexity reduces to n. Being diagonal means that all coefficients <math>a_{i,j} </math> but <math>a_{i,i}</math> are zeros leaving only one term in the sum <math>\sum a_{i,j} \vec e_i</math> above. The surviving diagonal elements, <math>a_{i,i}</math>, are known as '''eigenvalues''' and designated with <math>\lambda_i</math> in the defining equation, which reduces to <math>A \vec e_i = \lambda_i \vec e_i</math>. The resulting equation is known as '''eigenvalue equation'''.<ref>{{cite book |last=Nearing |first=James |year=2010 |title=Mathematical Tools for Physics |url=http://www.physics.miami.edu/nearing/mathmethods |chapter=Chapter 7.9: Eigenvalues and Eigenvectors |chapterurl= http://www.physics.miami.edu/~nearing/mathmethods/operators.pdf |accessdate=January 1, 2012|isbn=048648212X}}</ref> The [[Eigenvalues and eigenvectors|eigenvectors and eigenvalues are derived from it via the '''characteristic polynomial''']].
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| With [[Diagonalizable matrix#Diagonalization|diagonalization]], it is [[diagonalizability|often possible]] to [[change of basis|translate]] to and from eigenbases.
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| ==Examples in 2D graphics==
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| Most common geometric transformations that keep the origin fixed are linear, including rotation, scaling, shearing, reflection, and orthogonal projection; if an affine transformation is not a pure translation it keeps some point fixed, and that point can be chosen as origin to make the transformation linear. In two dimensions, linear transformations can be represented using a 2×2 transformation matrix.
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| ===Rotation===
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| For [[coordinate rotation|rotation]] by an angle θ '''clockwise''' about the origin (Note that this definition of clockwise is dependent on the x axis pointing right and the y axis pointing up. In for example [[SVG]], where the y axis points down, the below matrices must be swapped) the functional form is <math>x' = x \cos \theta + y \sin \theta</math> and <math>y' = - x \sin \theta + y \cos \theta</math>. Written in matrix form, this becomes:
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| :<math>
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| \begin{bmatrix} x' \\ y' \end{bmatrix} = \begin{bmatrix} \cos \theta & \sin\theta \\ -\sin \theta & \cos \theta \end{bmatrix} \begin{bmatrix} x \\ y \end{bmatrix}
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| </math>
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| Similarly, for a rotation '''counter clockwise''' about the origin, the functional form is <math>x' = x \cos \theta - y \sin \theta</math> and <math>y' = x \sin \theta + y \cos \theta</math> and the matrix form is:
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| :<math>
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| \begin{bmatrix} x' \\ y' \end{bmatrix} = \begin{bmatrix} \cos \theta & - \sin\theta \\ \sin \theta & \cos \theta \end{bmatrix} \begin{bmatrix} x \\ y \end{bmatrix}
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| </math>
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| ===Scaling===
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| For [[scaling (geometry)|scaling]] (that is, enlarging or shrinking), we have <math>x' = s_x \cdot x</math> and <math>y' = s_y \cdot y</math>. The matrix form is:
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| :<math>
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| \begin{bmatrix} x' \\ y' \end{bmatrix} = \begin{bmatrix} s_x & 0 \\ 0 & s_y \end{bmatrix} \begin{bmatrix} x \\ y \end{bmatrix}
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| </math>
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| When <math>\ s_x s_y = 1</math>, then the matrix is a [[squeeze mapping]] and preserves [[area]]s in the plane.
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| If <math>s_x</math> or <math>s_y</math> is greater than 1 in absolute value, the transformation stretches the figures in the corresponding direction; if less than 1, it shrinks them in that direction. Negative values of <math>s_x</math> or <math>s_y</math> also flips (mirrors) the points in that direction.
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| Applying this sort of scaling <math>k</math> times is equivalent to applying a single scaling with factors <math>s_x^k</math> and <math>s_y^k</math>.
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| More generally, any [[symmetric matrix|symmetric]] <math>n\times n</math> matrix defines a scaling along two perpendicular axes (the [[eigenvector]]s of the matrix) by equal or distinct factors (the [[eigenvalues]] corresponding to those eigenvectors).
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| ===Shearing===
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| For [[shear mapping]] (visually similar to slanting), there are two possibilities.
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| A shear parallel to the ''x'' axis has <math>x' = x + ky</math> and <math>y' = y</math>. Written in matrix form, this becomes:
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| :<math>
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| \begin{bmatrix} x' \\ y' \end{bmatrix} = \begin{bmatrix} 1 & k \\ 0 & 1 \end{bmatrix} \begin{bmatrix} x \\ y \end{bmatrix}
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| </math>
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| A shear parallel to the ''y'' axis has <math>x' = x</math> and <math>y' = y + kx</math>, which has matrix form:
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| :<math>
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| \begin{bmatrix} x' \\ y' \end{bmatrix} = \begin{bmatrix} 1 & 0 \\ k & 1 \end{bmatrix} \begin{bmatrix} x \\ y \end{bmatrix}
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| </math>
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| ===Reflection===
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| {{main|Householder transformation}}
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| To reflect a vector about a line that goes through the origin, let <math>\scriptstyle \vec{l} = (l_x, l_y)</math> be a [[vector (geometric)|vector]] in the direction of the line:
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| :<math>\mathbf{A} = \frac{1}{\lVert\vec{l}\rVert^2} \begin{bmatrix} l_x^2 - l_y^2 & 2 l_x l_y \\ 2 l_x l_y & l_y^2 - l_x^2 \end{bmatrix}</math>
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| ===Orthogonal projection===
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| To project a vector orthogonally onto a line that goes through the origin, let <math>\scriptstyle \vec{u} \,=\, (u_x, u_y)</math> be a [[vector (geometric)|vector]] in the direction of the line. Then use the transformation matrix:
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| :<math>
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| \mathbf{A} = \frac{1}{\lVert\vec{u}\rVert^2} \begin{bmatrix} u_x^2 & u_x u_y \\ u_x u_y & u_y^2 \end{bmatrix}
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| </math> | |
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| As with reflections, the orthogonal projection onto a line that does not pass through the origin is an affine, not linear, transformation.
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| [[Projection (linear algebra)|Parallel projection]]s are also linear transformations and can be represented simply by a matrix. However, perspective projections are not, and to represent these with a matrix, [[Homogeneous_coordinates#Use_in_computer_graphics|homogeneous coordinates]] must be used.
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| ==Examples in 3D graphics==
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| ===Rotation===
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| The matrix to rotate an angle ''θ'' about the axis defined by [[unit vector]] (''l'',''m'',''n'') is<ref>{{cite book | page = 154 | title = Basic Mathematics for Electronic Engineers:Models and Applications | first = John E. | last= Szymanski | publisher = Taylor & Francis | year = 1989 | isbn = 0278000681}}</ref>
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| :<math>\begin{bmatrix}
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| ll(1-\cos \theta)+\cos\theta & ml(1-\cos\theta)-n\sin\theta & nl(1-\cos\theta)+m\sin\theta\\
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| lm(1-\cos\theta)+n\sin\theta & mm(1-\cos\theta)+\cos\theta & nm(1-\cos\theta)-l\sin\theta \\
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| ln(1-\cos\theta)-m\sin\theta & mn(1-\cos\theta)+l\sin\theta & nn(1-\cos\theta)+\cos\theta
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| \end{bmatrix}.</math>
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| ===Reflection===
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| {{main|Householder transformation}}
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| To reflect a point through a plane <math>ax + by + cz = 0</math> (which goes through the origin), one can use <math>\mathbf{A} = \mathbf{I}-2\mathbf{NN}^T </math>, where <math>\mathbf{I}</math> is the 3x3 identity matrix and <math>\mathbf{N}</math> is the three-dimensional [[unit vector]] for the vector normal of the plane. If the [[L2 norm]] of <math>a, b,</math> and <math>c</math> is unity, the transformation matrix can be expressed as:
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| :<math>\mathbf{A} = \begin{bmatrix} 1 - 2 a^2 & - 2 a b & - 2 a c \\ - 2 a b & 1 - 2 b^2 & - 2 b c \\ - 2 a c & - 2 b c & 1 - 2c^2 \end{bmatrix}</math>
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| Note that these are particular cases of a [[Householder reflection]] in two and three dimensions. A reflection about a line or plane that does not go through the origin is not a linear transformation; it is an [[affine transformation]].
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| ==Composing and inverting transformations==
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| One of the main motivations for using matrices to represent linear transformations is that transformations can then be easily composed (combined) and inverted.
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| Composition is accomplished by [[matrix multiplication]]. If '''A''' and '''B''' are the matrices of two linear transformations, then the effect of applying first '''A''' and then '''B''' to a vector ''x'' is given by:
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| :<math>\mathbf{B}(\mathbf{A} \vec{x} ) = (\mathbf{BA}) \vec{x}</math>
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| (This is called the [[associative property]].)
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| In other words, the matrix of the combined transformation '''''A''' followed by '''B''''' is simply the product of the individual matrices. Note that the multiplication is done in the opposite order from the English sentence: the matrix of "'''A''' followed by '''B'''" is '''BA''', not '''AB'''.
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| A consequence of the ability to compose transformations by multiplying their matrices is that transformations can also be inverted by simply [[Invertible matrix|inverting their matrices]]. So, '''A'''<sup>−1</sup> represents the transformation that "undoes" '''A'''.
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| ==Other kinds of transformations==
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| ===Affine transformations===<!-- This section is linked from [[Affine transformation]] -->
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| To represent [[affine transformation]]s with matrices, we can use [[homogeneous coordinates]]. This means representing a 2-vector (''x'', ''y'') as a 3-vector (''x'', ''y'', 1), and similarly for higher dimensions. Using this system, translation can be expressed with matrix multiplication. The functional form <math>x' = x + t_x; y' = y + t_y</math> becomes:
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| :<math>
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| \begin{bmatrix} x' \\ y' \\ 1 \end{bmatrix} = \begin{bmatrix} 1 & 0 & t_x \\ 0 & 1 & t_y \\ 0 & 0 & 1 \end{bmatrix} \begin{bmatrix} x \\ y \\ 1 \end{bmatrix}.
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| </math>
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| All ordinary linear transformations are included in the set of affine transformations, and can be described as a simplified form of affine transformations. Therefore, any linear transformation can be also represented by a general transformation matrix. The latter is obtained by expanding the corresponding linear transformation matrix by one row and column, filling the extra space with zeros except for the lower-right corner, which must be set to 1. For example, ''the '''clockwise''' [[rotation matrix]] from above'' becomes:
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| :<math>\begin{bmatrix} \cos \theta & \sin \theta & 0 \\ -\sin \theta & \cos \theta & 0 \\ 0 & 0 & 1 \end{bmatrix}</math>
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| Using transformation matrices containing homogeneous coordinates, translations can be seamlessly intermixed with all other types of transformations. The reason is that the real plane is mapped to the ''w'' = 1 plane in real projective space, and so translation in real [[Euclidean space]] can be represented as a shear in real projective space. Although a translation is a non-linear transformation in a 2-D or 3-D Euclidean space described by Cartesian coordinates, it becomes, in a 3-D or 4-D projective space described by homogeneous coordinates, a simple linear transformation (a shear).
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| When using affine transformations, the homogeneous component of a coordinate vector (normally called ''w'') will never be altered. One can therefore safely assume that it is always 1 and ignore it. However, this is not true when using perspective projections.
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| ===Perspective projection===
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| {{see also|3D projection#Perspective projection|l1=Perspective projection}}
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| Another type of transformation, of importance in [[3D computer graphics]], is the [[perspective projection]]. Whereas parallel projections are used to project points onto the image plane along parallel lines, the perspective projection projects points onto the image plane along lines that emanate from a single point, called the center of projection. This means that an object has a smaller projection when it is far away from the center of projection and a larger projection when it is closer.
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| The simplest perspective projection uses the origin as the center of projection, and ''z'' = 1 as the image plane. The functional form of this transformation is then <math>x' = x / z</math>; <math>y' = y / z</math>. We can express this in [[homogeneous coordinates]] as:
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| :<math>
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| \begin{bmatrix} x_c \\ y_c \\ z_c \\ w_c \end{bmatrix} =
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| \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & 0 & 1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} \begin{bmatrix} x \\ y \\ z \\ w \end{bmatrix}
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| </math>
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| After carrying out the matrix multiplication, the homogeneous component ''w<sub>c</sub>'' will, in general, not be equal to 1. Therefore, to map back into the real plane we must perform the '''homogeneous divide''' or '''perspective divide''' by dividing each component by ''w<sub>c</sub>'':
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| :<math>
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| \begin{bmatrix} x' \\ y' \\ z' \end{bmatrix} = \frac{1}{w_c} \begin{bmatrix} x_c \\ y_c \\ z_c \end{bmatrix}
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| </math>
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| More complicated perspective projections can be composed by combining this one with rotations, scales, translations, and shears to move the image plane and center of projection wherever they are desired.
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| ==See also==
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| * [[3D projection]]
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| * [[Transformation (function)]]
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| ==References==
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| {{Reflist}}
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| ==External links==
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| * [http://web.archive.org/web/20091027131421/http://geocities.com/evilsnack/matrix.htm The Matrix Page] Practical examples in [[POV-Ray]]
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| * [http://mathworld.wolfram.com/RotationMatrix.html Reference page] - Rotation of axes
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| * [http://www.idomaths.com/linear_transformation.php Linear Transformation Calculator]
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| * [http://www.wiley.com/legacy/products/subject/life/biological_anthropology/0471205079_virtual_reconstruction/chapter5_trafo.html Transformation Applet] - Generate matrices from 2D transformations and vice versa.
| |
| {{Linear algebra}}
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| | |
| [[Category:Computer graphics]]
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| [[Category:Matrices]]
| |
| [[Category:Transformation (function)]]
| |
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