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Generalized singular value decomposition
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<p> In <a href="/facts/Linear_algebra/8VaB4VWk">linear algebra</a>, the generalized singular value decomposition (GSVD) is the name of two different techniques based on the <a href="/facts/Singular_value_decomposition/8t3v6wk3">singular value decomposition (SVD)</a>. The two versions differ because one version decomposes two matrices (somewhat like the <a href="/facts/Higher-order_singular_value_decomposition/JTtTDy39">higher-order or tensor SVD</a>) and the other version uses a set of constraints imposed on the left and right singular vectors of a single-matrix SVD. </p>