**Algorithm**

SMO is an iterative algorithm for solving the optimization problem described above. SMO breaks this problem into a series of smallest possible sub-problems, which are then solved analytically. Because of the linear equality constraint involving the Lagrange multipliers, the smallest possible problem involves two such multipliers. Then, for any two multipliers and, the constraints are reduced to:

and this reduced problem can be solved analytically.

The algorithm proceeds as follows:

- Find a Lagrange multiplier that violates the Karush–Kuhn–Tucker (KKT) conditions for the optimization problem.
- Pick a second multiplier and optimize the pair .
- Repeat steps 1 and 2 until convergence.

When all the Lagrange multipliers satisfy the KKT conditions (within a user-defined tolerance), the problem has been solved. Although this algorithm is guaranteed to converge, heuristics are used to choose the pair of multipliers so as to accelerate the rate of convergence.

Read more about this topic: Sequential Minimal Optimization

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