在三维空间中旋转

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引用次数: 0

摘要

三维空间中的旋转比平面中的旋转更复杂。其中一个原因是三维空间中的旋转通常不会交换。(举个例子!)正如费曼所说,我们对空间中的旋转没有很好的直觉,因为我们在日常生活中很少遇到它们。所以我们很难想象,如果我们把一个物体,相对于一个轴旋转一个角度,然后相对于另一个轴旋转另一个角度,会发生什么。如果我们是鱼或鸟,情况可能就不同了。我们考虑在原点有固定中心的旋转。那么旋转就是由行列式为1的正交矩阵表示的空间的线性变换。正交性意味着所有的距离和角度都保持不变。正交矩阵的行列式总是1或1。(想想为什么。)行列式等于1的条件意味着方向不变。一个行列式为1的正交变换将右鞋映射到左鞋上;这样的正交变换有时被称为反常旋转,在现实生活中我们实际上无法实现。(不管你怎么旋转左脚,它永远不会变成右脚)。我们从旋转矩阵的简单性质开始。定理1设A为旋转矩阵。那么1是a的一个特征值,另外两个特征值要么是- 1,要么是绝对值1的复共轭数。第一个表述意味着有一个向量x = 0保持不变,Ax = x,所以由这个x张成的一维子空间的每个向量保持不变,也就是说每次旋转都有一个轴。在维2中不是这样的。第2维的(非单位)旋转置换了每个非零向量。练习:给出一个4维旋转的例子,它移动每个非零向量。定理1的证明。首先注意到正交矩阵的所有特征值的绝对值为1。确实,Ax = λx, Ax 2 = |λ| 2 × 2, Ax 2 = x2,所以|λ| = 1。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rotations in 3-space
Rotations in 3-space are more complicated than rotations in a plane. One reason for this is that rotations in 3-space in general do not commute. (Give an example!) As Feynman says, we don't have a good intuition about rotations in space, because we rarely encounter them in our daily experience. So it is hard for us to imagine what will happen if we rotate a body with respect to one axis by a certain angle, then with respect to another axis by another angle and so on. It would be probably different if we were fish or birds. We consider rotations with fixed center which we place at the origin. Then rotations are linear transformations of the space represented by orthogonal matrices with determinant 1. Orthogonality means that all distances and angles are preserved. The determinant of an orthogonal matrix is always 1 or-1. (Think why.) The condition that determinant equals 1 means that orientation is preserved. An orthogonal transformation with determinant-1 will map a right shoe onto a left shoe; such orthogonal transformations are called sometimes improper rotations, we cannot actually perform them in real life. (No matter how you rotate a left shoe it will never become a right shoe). We begin with simple properties of rotation matrices. Theorem 1 Let A be a rotation matrix. Then 1 is an eigenvalue of A. Two other eigenvalues are either −1 or complex conjugate numbers of absolute value 1. The first statement means that there is a vector x = 0 which remains fixed, Ax = x. So each vector of the one-dimensional subspace spanned by this x remains fixed, that is every rotation has an axis. This is not so in dimension 2. A (non-identity) rotation in dimension 2 displaces every non-zero vector. Exercise: give an example of rotation in dimension 4 which moves every non-zero vector. Proof of Theorem 1. First notice that all eigenvalues of an orthogonal matrix have absolute value 1. Indeed, Ax = λx, Ax 2 = |λ| 2 x 2 , and Ax 2 = x 2 , so |λ| = 1.
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