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Sutton Monro coauthored the 1951 stochastic approximation paper with Herbert Robbins. The original title page verifies both full names and their joint contribution.^[ref]

The problem is to find where an unknown mean response $M(x)$ reaches a target $\alpha$ when only noisy observations are available. An update $x_{n+1}=x_n-a_n(Y_n-\alpha )$ moves according to the observed error. Positive step sizes with $\sum a_n=\infty$ and $\sum a_n^2<\infty$, together with suitable response and noise hypotheses, balance continued movement against accumulated noise. The method replaces exact root evaluation by a convergent statistical procedure.^[ref]