This paper presents two novel algorithms for approximately projecting symmetric matrices onto the Positive Semidefinite (PSD) cone using Randomized Numerical Linear Algebra (RNLA). Classical PSD projection methods rely on full-rank deterministic eigen-decomposition, which can be computationally prohibitive for large-scale problems. Our approach leverages RNLA to construct low-rank matrix approximations before projection, significantly reducing the required numerical resources. The first algorithm utilizes random sampling to generate a low-rank approximation, followed by a standard eigen-decomposition on this smaller matrix. The second algorithm enhances this process by introducing a scaling approach that aligns the leading-order singular values with the positive eigenvalues, ensuring that the low-rank approximation captures the essential information about the positive eigenvalues for PSD projection. Both methods offer a trade-off between accuracy and computational speed, supported by probabilistic error bounds. To further demonstrate the practical benefits of our approach, we integrate the randomized projection methods into a first-order Semi-Definite Programming (SDP) solver. Numerical experiments, including those on SDPs derived from Sum-of-Squares (SOS) programming problems, validate the effectiveness of our method, especially for problems that are infeasible with traditional deterministic methods.

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