Separable Convex Mixed-Integer Optimization: Improved Algorithms and Lower Bounds

We provide several novel algorithms and lower bounds in central settings of mixed-integer (non-)linear optimization, shedding new light on classic results in the field. This includes an improvement on record running time bounds obtained from a slight extension of Lenstra’s 1983 algorithm [Math. Oper. Res. ’83] to optimizing under few constraints with small coefficients. This is important for ubiquitous tasks like knapsack–, subset sum– or scheduling problems [Eisenbrand and Weismantel, SODA’18, Jansen and Rohwedder, ITCS’19].

Further, we extend our algorithm to an intermediate linear optimization problem when the matrix has many rows that exhibit 2-stage stochastic structure, which adds to a prominent line of recent results on this and similarly restricted cases [Jansen et al. ICALP’19, Cslovjecsek et al. SODA’21, Brand et al. AAAI’21, Klein, Reuter SODA’22, Cslovjecsek et al. SODA’24]. We also show that the generalization of two fundamental classes of structured constraints from these works (n-fold and 2-stage stochastic programs) to separable-convex mixed-integer optimization are harder than their mixed-integer, linear counterparts. This counters a widespread belief popularized initially by an influential paper of Hochbaum and Shanthikumar, namely that “convex separable optimization is not much harder than linear optimization” [J. ACM ’90].

To obtain our algorithms, we employ the mixed Graver basis introduced by Hemmecke [Math. Prog. ’03], and our work is the first to give bounds on the norm of its elements. Importantly, we use these bounds differently from how purely-integer Graver bounds are exploited in related approaches, and prove that, surprisingly, this cannot be avoided.