I use the following functions to receive a matrix, and make it symmetric positive definite:

isPD[mat_] := Module[{norm},
   norm = Norm[mat];
   Apply[And, Map[(# > 0) &, Eigenvalues[mat/norm]*norm]]];

positivizeMatrix[X_] := Module[{res},
   res = X;
   res = 0.5*(res + Transpose[res]);
   norm = Norm[res];
    auxsystDP = Eigensystem[res/norm];
    duDP = DiagonalMatrix[auxsystDP[[1]]]*norm;
    wDP = ReplacePart[duDP, {i_, i_} /; duDP[[i, i]] < 0 :> 0];
    lenRes = Length[res];
    powerDP = 30;
     res = 
        auxsystDP[[2]]].(wDP + 
     res = 0.5*(res + Transpose[res]);

Next, I have a function that simply does v.LinearSolve[mat, v, Method -> "Cholesky"];, where v is a constant vector, and mat is 'SPDized' matrix using the above positivizeMatrix.

Here an example where they don't work together...

LinearSolve[{{0.2767887221630757`, -0.20435493943670538`, 
   0.17471789061898896`}, {-0.20435493943670538`, 
   0.25577623175273967`, -0.243257996494102`}, {0.17471789061898896`, \
   0.23474884689385866`}}, {-0.7008470974262857`, \
-0.9457472725629061`, -1.654496598836136`}, Method -> "Cholesky"]

returns LinearSolve::npdef: The matrix {{0.276789,-0.204355,0.174718},{<<1>>},{0.174718,-0.243258,0.234749}} is not positive definite, whereas isPD returns True.

How can I create a positivize function that will work for any matrix dim, and be consistently with the linear solve, using the Cholesky method?

  • $\begingroup$ You use a modified (regularized) definition of positive definiteness. I can imagine a case where Norm is almost zero. The matrix would not be positive definite (eigenvalues essentially zero to machine precision). But if multiplied by a big number (1/Norm) the eigenvalues would become positive. $\endgroup$
    – yarchik
    Dec 1, 2020 at 21:05
  • $\begingroup$ @yarchik even if I do norm=Max[Norm[mat],1], the problem still persists... $\endgroup$ Dec 1, 2020 at 21:34


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