A convex quadratic semi-definite programming approach to the partial additive constant problem in multidimensional scaling
Benasseni [Partial additive constant, J. Statist. Comput. Simul. 49 (1994), pp. 179-193] studied the partial additive constant problem in multidimensional scaling. This problem is quite challenging to solve, and Benasseni proposed a numerical procedure for two special cases: the cross-set partial perturbation and the within-set partial perturbation. This paper casts the problem as a modern quadratic semi-definite programming (QSDP) problem, which is not only capable of dealing with general cases, but also enjoys a number of good properties. One of the good properties is that the proposed approach can find the minimal constant under very weak conditions. Another is that there exists a ready-to-use numerical package such as the QSDP solver in Toh [An inexact path-following algorithm for convex quadratic SDP, Math. Program. 112 (2008), pp. 221-254], allowing a great deal of flexibility in choosing the index set to which the partial constant should be added. Our numerical results show a significant improvement over that reported in Benasseni (1994).
partial additive constant multidimensional scaling positive semi-definite programming farkas' lemma lagrange multiplier