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node164.html
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<title>直接方法</title>
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<b>下一节:</b><a name="tex2html3231" href="node165.html">单向量和多向量迭代</a>
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<!--End of Navigation Panel--><h1><a name="SECTION001430000000000000000"></a> <a name="sec:ghepdirect"></a>
<a name="17137"></a>
<a name="17138"></a>
<a name="17139"></a>
直接方法
</h1>
<p>
在本节中,我们将简要讨论计算稠密矩阵特征值和特征向量的方法。通过<span class="math-inline">B</span>的分解(参见<a href="node163.html#B_chol">5.4</a>),广义厄米特征值问题(GHEP,参见<a href="node156.html#gsymeig">5.1</a>)被转化为标准厄米特征值问题(参见<a href="node163.html#C_eigenvalues">5.5</a>)。随后,可以采用第§<a href="node93.html#sec:dense">4.2</a>节中讨论的直接方法。
<p>
具体来说,在LAPACK库(参见[<a href="node421.html#lapack">12</a>])中,提供了以下驱动程序来解决<span class="math-inline">B</span>为正定矩阵的广义厄米特征值问题(GHEP,参见<a href="node156.html#gsymeig">5.1</a>):
<ul>
<li>简单驱动程序xSYGV计算所有特征值和(可选)特征向量。其底层算法是QR算法;参见第§<a href="node93.html#sec:dense">4.2</a>节。
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</li>
<li>专家驱动程序xSYGVX计算所有或选定的部分特征值和(可选)特征向量。如果所需的特征值或特征向量数量较少,专家驱动程序比简单驱动程序更快。该驱动程序使用QR算法、二分法和逆迭代中更高效的方法。
<a name="17151"></a>
</li>
<li>分治驱动程序xSYGVD解决与简单驱动程序相同的问题。对于大型矩阵,它比简单驱动程序快得多,但需要更多的内存空间。名称“分治”指的是其底层分治算法;参见第§<a href="node93.html#sec:dense">4.2</a>节。
<a name="17154"></a>
</li>
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对这些方法的数值分析表明,如果<span class="math-inline">B</span>相对于求逆是病态的,即条件数<span class="math-inline">\kappa_2(B) = \Vert B\Vert _2\Vert B^{-1}\Vert _2</span>很大,这些方法可能会在数值上不稳定,并且在计算的特征值和特征向量中产生较大误差。目前还没有任何直接方法可以直接应用于保持<span class="math-inline">A</span>和<span class="math-inline">B</span>对称性的<span class="math-inline">A</span>和<span class="math-inline">B</span>。另一种方法是应用QZ算法(参见第§<a href="node282.html#sec:qzalg">8.2</a>节),但这将失去对称性。
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<b>下一节:</b><a name="tex2html3231" href="node165.html">单向量和多向量迭代</a>
<b>上一级:</b><a name="tex2html3225" href="node155.html">广义厄米特征值问题</a>
<b>上一节:</b><a name="tex2html3219" href="node163.html">转换为标准问题</a>
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<address>
Susan Blackford
2000-11-20
</address>
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