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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
New Bounds on Augmenting Steps of Block-structured Intege...
Lin Chen, Lei Xu, Weidong Shi, Martin Koutecký · 2018-05-10 · via cs.DS updates on arXiv.org

We consider 4-block $n$-fold integer programs, whose constraint matrix consists of $n$ copies of small matrices $A$, $B$, and $D$, and one copy of $C$, in a specific block structure. All existing algorithms along this line of research follows an iterative augmentation framework, which relies on the so-called Graver basis of the constraint matrix that constitutes a set of fundamental augmenting steps. Bounding the $\ell_1$- or $\ell_\infty$-norm of elements of the Graver basis is the key to these algorithms. Hemmecke et al.~[Math. Prog. 2014] showed that 4-block $n$-fold IP has Graver elements of $\ell_\infty$-norm at most $O_{FPT}(n^{2^{s_{D}}})$, leading to an algorithm with a similar runtime; here, $s_{D}$ is the number of rows of matrix $D$ and $ O_{FPT}(1)$ hides a multiplicative factor that is only dependent on the small matrices $A,B,C,D$. We prove that the $\ell_{\infty}$-norm of the Graver elements of 4-block $n$-fold IP is upper bounded by $O_{FPT}(n^{s_{D}})$, improving significantly over the previous bound $O_{FPT} (n^{2^{s_{D}}})$. We also provide a matching lower bound of $Ω(n^{s_{D}})$ which even holds for arbitrary non-zero lattice elements, ruling out augmenting algorithm relying on even more restricted notions of augmentation than the Graver basis. We then consider a special case of 4-block $n$-fold in which $C$ is a zero matrix, called 3-block $n$-fold IP. We show that while even there the $\ell_{\infty}$-norm of its Graver elements is $Ω(n^{s_{D}})$, there exists a different decomposition into lattice elements whose $\ell_{\infty}$-norm is bounded by $ O_{FPT}(1)$, which allows us to provide improved upper bounds on the $\ell_{\infty}$-norm of Graver elements for 3-block $n$-fold IP.