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According to the paper "Computing a nearest symmetric positive semidefinite matrix"

Nicholas J. Higham, MR 943997 Computing a nearest symmetric positive semidefinite matrix, Linear Algebra Appl. 103 (1988), 103--118,

one of the solution is in the following form, $$A_{opt} = S + |\lambda| I$$ where $I$ is the identity matrix, $\lambda$ is the smallest negative eigenvalue of $S$

According to the paper "Computing a nearest symmetric positive semidefinite matrix" (1988), one of the solution is in the following form, $$A_{opt} = S + |\lambda| I$$ where $I$ is the identity matrix, $\lambda$ is the smallest negative eigenvalue of $S$

According to the paper

Nicholas J. Higham, MR 943997 Computing a nearest symmetric positive semidefinite matrix, Linear Algebra Appl. 103 (1988), 103--118,

one of the solution is in the following form, $$A_{opt} = S + |\lambda| I$$ where $I$ is the identity matrix, $\lambda$ is the smallest negative eigenvalue of $S$

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According to the paper "Computing a nearest symmetric positive semidefinite matrix" (1988), one of the solution is in the following form, $$A_{opt} = S + |\lambda| I$$ where $I$ is the identity matrix, $\lambda$ is the smallest negative eigenvalue of $S$