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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
Exponentially faster fixed-parameter algorithms for high-...
David Fischer, Julian Golak, Matthias Mnich · 2022-03-08 · via cs.DS updates on arXiv.org

We consider so-called $N$-fold integer programs (IPs) of the form $\max\{c^T x : Ax = b, \ell \leq x \leq u, x \in \mathbb Z^{nt}\}, where $A \in \mathbb Z^{(r+sn)\times nt} consists of $n$ arbitrary matrices $A^{(i)} \in \mathbb Z^{r\times t}$ on a horizontal, and $n$ arbitrary matrices $B^{(j)} \in \mathbb Z^{s\times t} on a diagonal line. Several recent works design fixed-parameter algorithms for $N$-fold IPs by taking as parameters the numbers of rows and columns of the $A$- and $B$-matrices, together with the largest absolute value $Δ$ over their entries. These advances provide fast algorithms for several well-studied combinatorial optimization problems on strings, on graphs, and in machine scheduling. In this work, we extend this research by proposing algorithms that additionally harness a partition structure of submatrices $A^{(i)}$ and $B^{(j)}$, where row indices of non-zero entries do not overlap between any two sets in the partition. Our main result is an algorithm for solving any $N$-fold IP in time $nt log(nt)L^2(S_A)^{O(r+s)}(p_Ap_BΔ)^{O(rp_Ap_B+sp_Ap_B)}$, where $p_A$ and $p_B$ are the size of the largest set in such a partition of $A^{(i)}$ and $B^{(j)}$, respectively, $S_A$ is the number of parts in the partition of $A = (A^{(1)},..., A^{(n)}), and $L = (log(||u - \ell||_\infty)\cdot (log(max_{x:\ell \leq x \leq u} |c^Tx|))$ is a measure of the input. We show that these new structural parameters are naturally small in high-multiplicity scheduling problems, such as makespan minimization on related and unrelated machines, with and without release times, the Santa Claus objective, and the weighted sum of completion times. In essence, we obtain algorithms that are exponentially faster than previous works by Knop et al. (ESA 2017) and Eisenbrand et al./Kouteck{ý} et al. (ICALP 2018) in terms of the number of job types.