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arXiv:2611.16820 · v1

Canonical Truncation for Mixed-Integer and Combinatorial n-Fold Optimization

首次提交 2026-11-12 21:55
版本更新 2026-11-12 21:55
雷达观测 2026-11-13 14:15
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摘要

We consider a special block-structured integer program with nn blocks. Every block has a prescribed number of vectors that need to be selected while minimizing the objective c⊺x+g(Ax)c^{\intercal}x + g(Ax), where cc is a rational cost vector, AA is an integral matrix with dd rows, and g ⁣:Zd→Q∪{+∞}g \colon \mathbb{Z}^d \to \mathbb{Q}\cup \{+\infty\} is an arbitrary function. We access gg via an oracle that minimizes c⊺x+g(Ax)c^{\intercal}x + g(Ax) when all but O(d)O(d) variables are fixed. Our algorithm runs in time (ndΔ)O(d)(Q+poly(∣I∣))(ndΔ)^{O(d)}(Q + \text{poly}(|I|)) where ΔΔ is the largest absolute value of an entry in AA, QQ is the necessary time to evaluate the oracle for gg and ∣I∣|I| is the size of the input. This model is a variation of the model studied by Dadush et al.~[IPCO 23]. Their model has only a single block but individual upper and lower bounds on the variables instead of prescribed block sums. For their model they present an algorithm with running time td(dΔ)O(d2)Qt^d(dΔ)^{O(d^2)}Q where tt is the number of variables. We use our algorithm to solve mixed-integer linear programs with no upper bounds on the variables, a linear objective, and arbitrarily many constraints involving only fractional variables in time (mΔ)O(m)poly(∣I∣)(mΔ)^{O(m)}\text{poly}(|I|), where mm is the number of constraints involving integral variables and ΔΔ is the largest absolute value of coefficients of integral variables. This improves the dependence on mm in the exponent to asymptotically match the dependency in the integer setting. Further, we use our algorithm to generalize the parameterized algorithm for scheduling on uniformly related machines by Rohwedder~[ICALP 25] to obtain an algorithm for multiple knapsack with running time wmax⁡O(d)poly(∣I∣)w_{\max}^{O(d)}\text{poly}(|I|) where wmax⁡w_{\max} is the maximum weight of an item and dd the number of different weights.

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v1 · 2026-11-12 21:55 · 最新观测版本

Canonical Truncation for Mixed-Integer and Combinatorial n-Fold Optimization

We consider a special block-structured integer program with nn blocks. Every block has a prescribed number of vectors that need to be selected while minimizing the objective c⊺x+g(Ax)c^{\intercal}x + g(Ax), where cc is a rational cost vector, AA is an integral matrix with dd rows, and g ⁣:Zd→Q∪{+∞}g \colon \mathbb{Z}^d \to \mathbb{Q}\cup \{+\infty\} is an arbitrary function. We access gg via an oracle that minimizes c⊺x+g(Ax)c^{\intercal}x + g(Ax) when all but O(d)O(d) variables are fixed. Our algorithm runs in time (ndΔ)O(d)(Q+poly(∣I∣))(ndΔ)^{O(d)}(Q + \text{poly}(|I|)) where ΔΔ is the largest absolute value of an entry in AA, QQ is the necessary time to evaluate the oracle for gg and ∣I∣|I| is the size of the input. This model is a variation of the model studied by Dadush et al.~[IPCO 23]. Their model has only a single block but individual upper and lower bounds on the variables instead of prescribed block sums. For their model they present an algorithm with running time td(dΔ)O(d2)Qt^d(dΔ)^{O(d^2)}Q where tt is the number of variables. We use our algorithm to solve mixed-integer linear programs with no upper bounds on the variables, a linear objective, and arbitrarily many constraints involving only fractional variables in time (mΔ)O(m)poly(∣I∣)(mΔ)^{O(m)}\text{poly}(|I|), where mm is the number of constraints involving integral variables and ΔΔ is the largest absolute value of coefficients of integral variables. This improves the dependence on mm in the exponent to asymptotically match the dependency in the integer setting. Further, we use our algorithm to generalize the parameterized algorithm for scheduling on uniformly related machines by Rohwedder~[ICALP 25] to obtain an algorithm for multiple knapsack with running time wmax⁡O(d)poly(∣I∣)w_{\max}^{O(d)}\text{poly}(|I|) where wmax⁡w_{\max} is the maximum weight of an item and dd the number of different weights.

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