Added L*D*L.T decomposition for real matrices.#1065
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Few comments from me.
BTW, I'm not sure we need this: numpy has no such method. Also, we have cholesky(), which can be used to get this decomposition.
Lets someone else decide if we must have this.
| u_conj_T = U.apply(lambda x: ctx.conj(x)).T | ||
| return v_conj_T * Splus * u_conj_T | ||
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| def ldlt(ctx, A): |
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I'm not sure about naming. numpy has no such function, scipy has linalg.ldl, which handle also hermitian matrices.
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I could probably make it handle Hermitian matrices ... also if not needed, no hurt feelings here.
Co-authored-by: Sergey B Kirpichev <skirpichev@gmail.com>
Co-authored-by: Sergey B Kirpichev <skirpichev@gmail.com>
Co-authored-by: Sergey B Kirpichev <skirpichev@gmail.com>
Co-authored-by: Sergey B Kirpichev <skirpichev@gmail.com>
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Added$L\cdot D\cdot L^T$ decomposition for real matrices.