Singulaariarvohajotelma-laskin

Määritä matriisin $$$\left[\begin{array}{ccc}0 & 1 & 1\\\sqrt{2} & 2 & 0\\0 & 1 & 1\end{array}\right]$$$ SVD.

Laske matriisin transpoosi: $$$\left[\begin{array}{ccc}0 & 1 & 1\\\sqrt{2} & 2 & 0\\0 & 1 & 1\end{array}\right]^{T} = \left[\begin{array}{ccc}0 & \sqrt{2} & 0\\1 & 2 & 1\\1 & 0 & 1\end{array}\right]$$$ (vaiheet: katso matriisin transpoosilaskin).

Kerro matriisi transpoosillaan: $$$W = \left[\begin{array}{ccc}0 & 1 & 1\\\sqrt{2} & 2 & 0\\0 & 1 & 1\end{array}\right]\cdot \left[\begin{array}{ccc}0 & \sqrt{2} & 0\\1 & 2 & 1\\1 & 0 & 1\end{array}\right] = \left[\begin{array}{ccc}2 & 2 & 2\\2 & 6 & 2\\2 & 2 & 2\end{array}\right]$$$ (vaiheiden näkemiseksi katso matriisikertolaskin).

Seuraavaksi etsi $$$W$$$:n ominaisarvot ja ominaisvektorit (vaiheittaiset ohjeet: katso ominaisarvojen ja -vektorien laskin).

Ominaisarvo: $$$8$$$, ominaisvektori: $$$\left[\begin{array}{c}1\\2\\1\end{array}\right]$$$.

Ominaisarvo: $$$2$$$, ominaisvektori: $$$\left[\begin{array}{c}1\\-1\\1\end{array}\right]$$$.

Ominaisarvo: $$$0$$$, ominaisvektori: $$$\left[\begin{array}{c}-1\\0\\1\end{array}\right]$$$.

Etsi nollasta poikkeavien ominaisarvojen ($$$\sigma_{i}$$$) neliöjuuret:

$$$\sigma_{1} = 2 \sqrt{2}$$$

$$$\sigma_{2} = \sqrt{2}$$$

Matriisi $$$\Sigma$$$ on nollamatriisi, jonka diagonaalilla on $$$\sigma_{i}$$$: $$$\Sigma = \left[\begin{array}{ccc}2 \sqrt{2} & 0 & 0\\0 & \sqrt{2} & 0\\0 & 0 & 0\end{array}\right]$$$.

Matriisin $$$U$$$ sarakkeet ovat normalisoidut (yksikkö)vektorit: $$$U = \left[\begin{array}{ccc}\frac{\sqrt{6}}{6} & \frac{\sqrt{3}}{3} & - \frac{\sqrt{2}}{2}\\\frac{\sqrt{6}}{3} & - \frac{\sqrt{3}}{3} & 0\\\frac{\sqrt{6}}{6} & \frac{\sqrt{3}}{3} & \frac{\sqrt{2}}{2}\end{array}\right]$$$ (yksikkövektorin löytämisen vaiheet, katso yksikkövektorilaskin).

Nyt, $$$v_{i} = \frac{1}{\sigma_{i}}\cdot \left[\begin{array}{ccc}0 & 1 & 1\\\sqrt{2} & 2 & 0\\0 & 1 & 1\end{array}\right]^{T}\cdot u_{i}$$$:

$$$v_{1} = \frac{1}{\sigma_{1}}\cdot \left[\begin{array}{ccc}0 & 1 & 1\\\sqrt{2} & 2 & 0\\0 & 1 & 1\end{array}\right]^{T}\cdot u_{1} = \frac{1}{2 \sqrt{2}}\cdot \left[\begin{array}{ccc}0 & \sqrt{2} & 0\\1 & 2 & 1\\1 & 0 & 1\end{array}\right]\cdot \left[\begin{array}{c}\frac{\sqrt{6}}{6}\\\frac{\sqrt{6}}{3}\\\frac{\sqrt{6}}{6}\end{array}\right] = \left[\begin{array}{c}\frac{\sqrt{6}}{6}\\\frac{\sqrt{3}}{2}\\\frac{\sqrt{3}}{6}\end{array}\right]$$$ (vaiheista katso matriisin skalaarikertolaskin ja matriisikertolaskin).

$$$v_{2} = \frac{1}{\sigma_{2}}\cdot \left[\begin{array}{ccc}0 & 1 & 1\\\sqrt{2} & 2 & 0\\0 & 1 & 1\end{array}\right]^{T}\cdot u_{2} = \frac{1}{\sqrt{2}}\cdot \left[\begin{array}{ccc}0 & \sqrt{2} & 0\\1 & 2 & 1\\1 & 0 & 1\end{array}\right]\cdot \left[\begin{array}{c}\frac{\sqrt{3}}{3}\\- \frac{\sqrt{3}}{3}\\\frac{\sqrt{3}}{3}\end{array}\right] = \left[\begin{array}{c}- \frac{\sqrt{3}}{3}\\0\\\frac{\sqrt{6}}{3}\end{array}\right]$$$ (vaiheista katso matriisin skalaarikertolaskin ja matriisikertolaskin).

Koska nollasta poikkeavia $$$\sigma_{i}$$$ ei enää ole ja tarvitsemme vielä yhden vektorin, etsi kaikille löydetyille vektoreille ortogonaalinen vektori löytämällä sen matriisin nollitila, jonka rivit ovat löydetyt vektorit: $$$\left[\begin{array}{c}\sqrt{2}\\-1\\1\end{array}\right]$$$ (vaiheista ks. nollitilan laskin).

Normalisoi vektori: tuloksena on $$$\left[\begin{array}{c}\frac{\sqrt{2}}{2}\\- \frac{1}{2}\\\frac{1}{2}\end{array}\right]$$$, (vaiheet: katso yksikkövektorilaskin).

Siispä $$$V = \left[\begin{array}{ccc}\frac{\sqrt{6}}{6} & - \frac{\sqrt{3}}{3} & \frac{\sqrt{2}}{2}\\\frac{\sqrt{3}}{2} & 0 & - \frac{1}{2}\\\frac{\sqrt{3}}{6} & \frac{\sqrt{6}}{3} & \frac{1}{2}\end{array}\right].$$$

Matriisit $$$U$$$, $$$\Sigma$$$ ja $$$V$$$ ovat sellaiset, että alkuperäinen matriisi $$$\left[\begin{array}{ccc}0 & 1 & 1\\\sqrt{2} & 2 & 0\\0 & 1 & 1\end{array}\right] = U \Sigma V^T$$$.

$$$U = \left[\begin{array}{ccc}\frac{\sqrt{6}}{6} & \frac{\sqrt{3}}{3} & - \frac{\sqrt{2}}{2}\\\frac{\sqrt{6}}{3} & - \frac{\sqrt{3}}{3} & 0\\\frac{\sqrt{6}}{6} & \frac{\sqrt{3}}{3} & \frac{\sqrt{2}}{2}\end{array}\right]\approx \left[\begin{array}{ccc}0.408248290463863 & 0.577350269189626 & -0.707106781186548\\0.816496580927726 & -0.577350269189626 & 0\\0.408248290463863 & 0.577350269189626 & 0.707106781186548\end{array}\right]$$$A

$$$\Sigma = \left[\begin{array}{ccc}2 \sqrt{2} & 0 & 0\\0 & \sqrt{2} & 0\\0 & 0 & 0\end{array}\right]\approx \left[\begin{array}{ccc}2.82842712474619 & 0 & 0\\0 & 1.414213562373095 & 0\\0 & 0 & 0\end{array}\right]$$$A

$$$V = \left[\begin{array}{ccc}\frac{\sqrt{6}}{6} & - \frac{\sqrt{3}}{3} & \frac{\sqrt{2}}{2}\\\frac{\sqrt{3}}{2} & 0 & - \frac{1}{2}\\\frac{\sqrt{3}}{6} & \frac{\sqrt{6}}{3} & \frac{1}{2}\end{array}\right]\approx \left[\begin{array}{ccc}0.408248290463863 & -0.577350269189626 & 0.707106781186548\\0.866025403784439 & 0 & -0.5\\0.288675134594813 & 0.816496580927726 & 0.5\end{array}\right]$$$A