This academic paper introduces Linformer, a novel approach to address the computational bottleneck of Transformer models in natural language processing. The authors demonstrate that the self-attention mechanism, which is a core component of Transformers and typically incurs quadratic time and space complexity with respect to sequence length, can be approximated by a low-rank matrix. By exploiting this finding, the Linformer reduces this complexity to linear time and space (O(n)), making it significantly more efficient for long sequences. The research provides both theoretical proofs and empirical evidence that the Linformer performs comparably to standard Transformers while offering substantial speed and memory improvements during both training and inference.