Tight Complexity Lower Bounds for Integer Linear Programming with Few Constraints

D. Knop, Michal Pilipczuk, Marcin Wrochna
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引用次数: 29

Abstract

We consider the standard ILP Feasibility problem: given an integer linear program of the form {Ax = b, x ⩾ 0}, where A is an integer matrix with k rows and ℓ columns, x is a vector of ℓ variables, and b is a vector of k integers, we ask whether there exists x ∈ N ℓ that satisfies Ax = b. Each row of A specifies one linear constraint on x; our goal is to study the complexity of ILP Feasibility when both k, the number of constraints, and ‖A‖∞, the largest absolute value of an entry in A, are small. Papadimitriou was the first to give a fixed-parameter algorithm for ILP Feasibility under parameterization by the number of constraints that runs in time ((‖A‖∞ + ‖b‖∞) ⋅ k)O(k2). This was very recently improved by Eisenbrand and Weismantel, who used the Steinitz lemma to design an algorithm with running time (k‖A‖∞)O(k) ⋅ log ‖b‖∞, which was subsequently refined by Jansen and Rohwedder to O(√ k‖A‖∞)k ⋅ log (‖ A‖∞ + ‖b‖∞) ⋅ log ‖A‖∞. We prove that for {0, 1}-matrices A, the running time of the algorithm of Eisenbrand and Weismantel is probably optimal: an algorithm with running time 2o(k log k) ⋅ (ℓ + ‖b‖∞)o(k) would contradict the exponential time hypothesis. This improves previous non-tight lower bounds of Fomin et al. We then consider integer linear programs that may have many constraints, but they need to be structured in a “shallow” way. Precisely, we consider the parameter dual treedepth of the matrix A, denoted tdD(A), which is the treedepth of the graph over the rows of A, where two rows are adjacent if in some column they simultaneously contain a non-zero entry. It was recently shown by Koutecký et al. that ILP Feasibility can be solved in time ‖A‖∞2O(tdD(A)) ⋅ (k + ℓ + log ‖b‖∞)O(1). We present a streamlined proof of this fact and prove that, again, this running time is probably optimal: even assuming that all entries of A and b are in {−1, 0, 1}, the existence of an algorithm with running time 22o(tdD(A)) ⋅ (k + ℓ)O(1) would contradict the exponential time hypothesis.
少约束整数线性规划的紧复杂度下界
我们考虑标准的ILP可行性问题:给定形式为{Ax = b, x大于或等于0}的整数线性规划,其中A是具有k行和r列的整数矩阵,x是包含r变量的向量,b是包含k个整数的向量,我们询问是否存在x∈N N满足Ax = b。A的每一行指定x上的一个线性约束;我们的目标是研究当约束数量k和A中条目的最大绝对值‖A‖∞都很小时ILP可行性的复杂性。Papadimitriou是第一个用运行时间((‖a‖∞+‖b‖∞)·k)O(k2)的约束数参数化ILP可行性的定参数算法。最近,Eisenbrand和Weismantel改进了这一点,他们使用Steinitz引理设计了一个运行时间(k‖A‖∞)O(k)⋅log‖b‖∞的算法,随后由Jansen和Rohwedder将其改进为O(√k‖A‖∞)k⋅log(‖A‖∞+‖b‖∞)⋅log‖A‖∞。我们证明了对于{0,1}-矩阵A, Eisenbrand和Weismantel算法的运行时间可能是最优的:运行时间为20 (k log k)⋅(h +‖b‖∞)o(k)的算法将违背指数时间假设。这改进了先前Fomin等人的非紧下界。然后我们考虑可能有许多约束的整数线性规划,但它们需要以一种“浅层”的方式结构化。准确地说,我们考虑矩阵A的参数对偶树深,记作tdD(A),它是图在A的行上的树深,其中两行相邻,如果在某些列中它们同时包含一个非零条目。最近,Koutecký等人证明了ILP可行性可以在时间上求解‖A‖∞2O(tdD(A))⋅(k + r + log‖b‖∞)O(1)。我们对这一事实给出了一个简化的证明,并再次证明,这个运行时间可能是最优的:即使假设a和b的所有条目都在{−1,0,1}中,存在一个运行时间为220 (tdD(a))⋅(k + r)O(1)的算法将与指数时间假设相矛盾。
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