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Primal Active-Set method for constrained convex quadratic problem

Filed under: Algorithms — Tags: , — Davor @ 10:05

I made an implementation of the Active-Set Method for Convex QP as described in Nocedal, J. e.a., Numerical Optimization, 2ed, 2006, p.472. The code in R can be found on Github. Output with two examples from the book can be found here.

There is an other free package named quadprog that does the same but with the limitation of only accepting positive definite matrices. This can be tweaked to work with positive semi-definite matrices. For example one can convert a positive semi-definite matrix to its nearest definite one with Matrix::nearPD() function. To cope with semi-definiteness in the Active-Set method, Moore-Penrose generalized inverse is used for solving the KKT problem.

Note that the Active-Set method must start with an initial feasible point. Understanding the problem is usually enough to calculate one. Nocedal describes a generic “Phase I” approach for finding a feasible point on p.473.

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