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Abstract Given graph$$G=(V,E)$$ $G=(V,E)$with vertex setVand edge setE, the maxk-cut problem seeks to partition the vertex setVinto at mostksubsets that maximize the weight (number) of edges with endpoints in different parts. This paper proposes a graph folding procedure (i.e., a procedure that reduces the number of the vertices and edges of graphG) for the weighted maxk-cut problem that may help reduce the problem’s dimensionality. While our theoretical results hold for any$$k \ge 2$$ $k\ge 2$, our computational results show the effectiveness of the proposed preprocessonlyfor$$k=2$$ $k=2$and on two sets of instances. Furthermore, we observe that the preprocess improves the performance of a MIP solver on a set of large-scale instances of the max cut problem. 

The numerical performance of algorithms can be studied using test sets or procedures that generate such problems. This paper proposes various methods for generating linear, semidefinite, and secondorder cone optimization problems. Specifically, we are interested in problem instances requiring a known optimal solution, a known optimal partition, a specific interior solution, or all these together. In the proposed problem generators, different characteristics of optimization problems, including dimension, size, condition number, degeneracy, optimal partition, and sparsity, can be chosen to facilitate comprehensive computational experiments. We also develop procedures to generate instances with a maximally complementary optimal solution with a predetermined optimal partition to generate challenging semidefinite and second-order cone optimization problems. Generated instances enable us to evaluate efficient interior-point methods for conic optimization problems.