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Sequential linear-quadratic programming is an iterative method for nonlinear optimization problems where objective function and constraints are twice continuously differentiable. Similarly to sequential quadratic programming , SLQP proceeds by solving a sequence of optimization subproblems. The difference between the two approaches is that:

This decomposition makes SLQP suitable to large-scale optimization problems, for which efficient LP and EQP solvers are available, these problems being easier to scale than full-fledged quadratic programs.

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