An Euclidean Distance Matrix Optimization Approach for Robust Multidimensional Scaling Seminar

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Kruskal’s stress minimization, though nonconvex and nonsmooth, has been a major computational model for dissimilarity data in multidimensional scaling. Semidefinite Programming (SDP) relaxation (by dropping the rank constraint) would lead to a high number of SDP cone constraints. This has rendered the SDP approach computationally challenging even for problems of small size. We reformulate the stress as an Euclidean Distance Matrix (EDM) optimization with box constraints under the help of the idea of Robust Multidimensional Scaling. A key element in our approach is the conditional positive semidefinite cone with rank cut. Although nonconvex, this geometric object allows a fast computation of the projection onto it and it naturally leads to a majorization-minimization algorithm with the minimization step having a closed-form solution. Moreover, we prove that our EDM optimization follows a continuously differentiable path, which greatly facilitated the analysis of the convergence to a stationary point. The superior performance of the proposed algorithm is demonstrated against some of the state-of-the-art solvers in the field of sensor network localization and molecular conformation. For instance, only 190 seconds needed to solve a problem with over 16,000,000 variables, which is far faster than current methods.