📄 svd.html
字号:
<HTML><HEAD><TITLE>Newmat09 - Singular value decomposition</TITLE></HEAD><BODY><H2>Singular value decomposition</H2><A HREF="evalues.html"> next</A> - <A HREF="evalues.html"> skip</A> - <A HREF="refer.html"> up</A> - <A HREF="index.html"> start</A><P>The singular value decomposition of an m x n <TT>Matrix</TT> <TT>A</TT>(where m >= n) is a decomposition<PRE> A = U * D * V.t()</PRE>where <TT>U</TT> is m x n with <TT>U.t() * U</TT> equalling the identity,<TT>D</TT> is an n x n<TT>DiagonalMatrix</TT> and <TT>V</TT> is an n x n orthogonal matrix(type <TT>Matrix</TT> in <I>Newmat</I>).<P>Singular value decompositions are useful for understanding the structureof ill-conditioned matrices, solving least squares problems, and forfinding the eigenvalues of <TT>A.t() * A</TT>.<P>To calculate the singular value decomposition of <TT>A</TT>(with m >= n) use one of<PRE> SVD(A, D, U, V); // U = A is OK SVD(A, D); SVD(A, D, U); // U = A is OK SVD(A, D, U, false); // U (can = A) for workspace only SVD(A, D, U, V, false); // U (can = A) for workspace only</PRE>where <TT>A</TT>, <TT>U</TT> and <TT>V</TT> are of type <TT>Matrix</TT> and<TT>D</TT>is a <TT>DiagonalMatrix</TT>. The values of <TT>A</TT> are not changed unless <TT>A</TT> is alsoinserted as the third argument.<P><A HREF="evalues.html"> next</A> - <A HREF="evalues.html"> skip</A> - <A HREF="refer.html"> up</A> - <A HREF="index.html"> start</A><P></BODY></HTML>⌨️ 快捷键说明
复制代码
Ctrl + C
搜索代码
Ctrl + F
全屏模式
F11
切换主题
Ctrl + Shift + D
显示快捷键
?
增大字号
Ctrl + =
减小字号
Ctrl + -