Solving the Shortest Vector Problem in $2^{0.6039n}$ Time
New algorithms solve the shortest vector problem in $2^{0.6039n}$ time, improving on previous methods.
New randomized algorithms address the shortest vector problem (SVP) in $n$-dimensional lattices, achieving a time complexity of $2^{0.6039n+o(n)}$ classically and $2^{0.5411n+o(n)}$ quantumly. This marks a significant improvement over previous algorithms running in $2^{n+o(n)}$ time. The algorithms leverage the Hessian property of the periodic Gaussian function at half the shortest vector to enhance performance.