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Recent progress in solving quadratic assignment problems (QAPs) from the QAPL1B (Quadratic Assignment Problem Library) test set has come from mixed-integer linear or quadratic programming models that are solved in [...]

We derive a new lower bound for the bandwidth of a graph that is based on a new lower bound for the min-cut problem. Our new semidefinite programming relaxation of [...]

We consider both facial reduction, FR , and symmetry reduction, SR , techniques for semidefinite programming, SDP . We show that the two together fit surprisingly well in an alternating direction method of multipliers, ADMM , approach. In fact, this approach allows for simply adding on nonnegativity constraints, and solving the doubly nonnegative, DNN  , relaxation of many classes of hard combinatorial problems. We also show that the singularity degree remains the same after SR , and that the DNN  relaxations considered here have singularity degree one, that is reduced to zero after FR . The combination of FR  and SR  leads to a significant improvement in both numerical stability and running time for both the ADMM  and interior point approaches. We test our method on various DNN  relaxations of hard combinatorial problems including quadratic assignment problems with sizes of more than n = 500 . This translates to a semidefinite constraint of order 250, 000 and 625 × 10 8 nonnegative constrained variables, before applying the reduction techniques., (© The Author(s) 2022.)