The four papers we review dated from 1967 up to two papers in 2025 collectively discuss the mathematical properties and deep learning applications of **doubly stochastic matrices**, which are nonnegative matrices whose rows and columns sum to one. One paper, "Concerning Nonnegative Matrices and Doubly Stochastic Matrices," provides the **foundational mathematical theory** regarding the convergence of iterative row and column scaling (known as the Sinkhorn algorithm) to a unique doubly stochastic matrix, contingent on the original matrix having "total support." The other papers focus on **Transformer architecture enhancements**, proposing "Sinkformers" and "Sparse Sinkhorn Attention" as variants that replace the standard row-wise SoftMax attention with the Sinkhorn algorithm to enforce **doubly stochastic attention matrices** for improved performance and theoretical properties, such as a connection to the Wasserstein metric. Furthermore, the "Gradient Multi-Normalization" paper introduces a **stateless optimizer** that uses a multi-normalization procedure, including a "Square-Root Sinkhorn" variant, demonstrating its efficacy and efficiency in training large language models.Sources:1967:CONCERNING NONNEGATIVE MATRICES AND DOUBLY STOCHASTIC MATRICEShttps://projecteuclid.org/journalArticle/Download?urlId=pjm%2F1102992505June 24, 2022:Sinkformers: Transformers with Doubly Stochastic Attentionhttps://arxiv.org/pdf/2110.11773February 10, 2025:Gradient Multi-Normalization for Stateless and Scalable LLM Traininghttps://arxiv.org/pdf/2502.06742July 12, 2025:ESPFormer: Doubly-Stochastic Attention with Expected Sliced Transport Planshttps://arxiv.org/pdf/2502.07962

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