Successive linear programming
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Successive Linear Programming (SLP) is an extension of the technique of linear programming to allow the optimisation of nonlinear programming problems through a series of linear approximations.
Starting at some estimate of the optimal solution, the method is based on solving successive first order approximations (i.e. linearizations) of the model. The linearizations are linear programming problems, which can be solved efficiently. As the linearizations are often non-bounded by themselves, and also to handle cases when the optimum lies in the interior of the feasible region, the method is typically applied with the combination of some step bounding technique like the trust region method. [1]
SLP, along with its second order analog sequential quadratic programming is widely used for process engineering problems.[citation needed]
References
- ^ Bazaraa, Mokhtar S.; Sheraly, Hanif D.; Shetty, C.M. (1993), Nonlinear Programming, Theory and Applications (2nd ed.), John Wiley & Sons, p. 432, ISBN 0-471-55793-5.
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