Recent content by rs1n

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    Rotation matrix -- Question

    Rotating the object by an angle of \theta is the same as rotating the coordinate system by -\theta -- it's all relative. For all intents and purposes, you can just consider only rotating the object (and changing the angle to its negation where needed). The second version is basically the same...
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    Cauchy Schwarz equality implies parallel

    To get back to the problem, though... over the complex numbers, the inner product is presumably a Hermitian inner product. So ##\begin{align*} \| u + v \|^2 & = \langle u + v, u+v \rangle = \langle u,u \rangle + \langle u,v \rangle + \langle v,u \rangle + \langle v, v \rangle\\ & = \langle u,u...
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    Cauchy Schwarz equality implies parallel

    You are absolutely right! My eyes failed me, somehow.
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    Logic behind normal line in expressing plane

    To plot by hand, it is generally much easier to plot where the plane intersects one of the 8 octants. For example, given ax+by+cz=d, set x=0 and you get by+cz=d. This produces a line that you can graph on the yz-plane. Similarly, set y=0 to get ax+cz=d -- a line in the xz-plane. Lastly, ax+by=d...
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    Cauchy Schwarz equality implies parallel

    By definition, \langle v_1, v_2 \rangle = \| v_1 \| \cdot \| v_2 \| \cdot \cos(\theta) where \theta is the angle between vectors v_1 and v_2. If you also additionally know that \langle v_1, v_2 \rangle = \| v_1 \| \cdot \| v_2 \| , then the angle between the two vectors must either be 0 or 180...
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    A possible way to integrate dx^2?

    The source (of the original problem) probably uses a different convention for mutiple integration. It looks like Cauchy's formula. From wikipedia: https://upload.wikimedia.org/math/4/9/9/4996ac5454f0b1e6a964f1ec572ba146.png
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    Matrix with repeated eigenvalues is diagonalizable....?

    If all n eigenvalues are distinct, then the matrix is diagonalizable. However, the converse is not true. There are matrices that are diagonalizable even if their eigenvalues are not distinct, as your example clearly shows.
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    Optimizing Across Noisy Domain

    Would the SVD be helpful? It would presumably still require you to compute the entire surface, though.
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    Easiest way to learn exact values for trig functions?

    If you look at the picture here: http://etc.usf.edu/clipart/43200/43216/unit-circle8_43216_lg.gif you will see that only the first quadrant is needed (and even then, only the angles between 0 and 45 degrees are needed (everything else can be derived by symmetry of the circle. As for the...
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    How to Memorize Even and Odd Functions?

    If f(-x) = f(x) then the function is even. If f(-x) = -f(x) then the function is odd. Example: f(x)=2x^4+4x^2-1 is even since f(-x) = 2(-x)^4+4(-x)^2-1 = 2x^4+4x^2-1 = f(x). Example: f(x)=x^3-3x is odd since f(-x) = (-x)^3 - 3(-x) = -x^3 + 3x = -(x^3-3x) = -f(x) Exaple: f(x)=x^2-x is neither...
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    When is the Cauchy-Schwartz inequality as large as possible?

    If you write the summations as dot products, you can readily see the answer. Let \vec{x} = \langle x_1, x_2, \dotsm, x_n \rangle and \vec{y} = \langle y_1, y_2, \dotsm, y_n \rangle. Then the Cauchy-Schwartz inequality can be restated as: \underbrace{(\vec{x} \cdot \vec{x})}_{\text{dot product}}...
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    Connection between subspace, span, and basis?

    It seems perhaps your issue is with the definitions. Then span of a set of n vectors, written as span(\{ v_1, v_2, \dotsm , v_n \}) is the set of all possible linear combinations of those n vectors. A basis is a collection of linearly independent vectors whose span is a vector space. To verify...
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    Looking for insight into what the Determinant means....

    The determinant can be interpreted geometrically. If A is an n\times n matrix, then let r_1, r_2, \dotsm, r_n be the n rows of A. The absolute value of the determinant of A would be the n-dimensional voume of the parallelotope corresponding to these n vectors. (Imagine one corner of the...
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    Estimating singular values from QR decomposition

    Yes, this is mathematically sound. A and R would have the same singular values.
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    Estimating singular values from QR decomposition

    Did you meant to write that the singular values of A are the square root of the eigenvalues of A^HA ?
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