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This paper studies the beamforming optimization challenge in reconfigurable intelligent surface (RIS)-aided multiple-input single-output (MISO) systems, where the RIS phase configuration is discrete. Conventional optimization methods for this discrete optimization problem necessitate resource-intensive exponential search and thus fall within the universal (NP-hard) category. We formally define this task as a discrete inner product maximization problem. Leveraging the inherent structure of this problem, we propose an efficient divide-and-sort (DaS) search algorithm to reach the global optimality for the maximization problem. The complexity of the proposed algorithm can be minimized to $\mathcal{O}(2^BN)$, a linear correlation with the count of phase discrete levels $2^B$ and reflecting units $N$. This is notably lower than the exhaustive search complexity of $\mathcal{O}(2^{BN})$. Numerical evaluations and experiments over real prototype also demonstrate the efficiency of the proposed DaS algorithm. Finally, by using the proposed algorithm, we show that over some resolution quantization level on each RIS unit (4-bit and above), there is no noticeable difference in power gains between continuous and discrete phase configurations.