Sequential minimal optimization

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Sequential minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem that arises during the training of support-vector machines (SVM).

It was invented by John Platt in 1998 at Microsoft Research.[1]

SMO is widely used for training support vector machines and is implemented by the popular LIBSVM tool.[2][3]

The publication of the SMO algorithm in 1998 has generated a lot of excitement in the SVM community, as previously available methods for SVM training were much more complex and required expensive third-party QP solvers.[4]
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https://en.wikipedia.org/wiki/Sequential_minimal_optimization