arXiv AI
Jul 28
Maximum Satisfiability of Simple Temporal Problems
arXiv:2607. 23785v1 Announce Type: cross Abstract: The Simple Temporal Problem (STP) is a core framework for quantitative temporal constraints.
Maximum Satisfiability of Simple Temporal Problems
The Simple Temporal Problem (STP) is a core framework for quantitative temporal constraints. As STP data can be inconsistent, we study MAXSTP: compute a maximum-cardinality consistent subset of constraints.
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arXiv Machine Learning
Jul 14
Precedence-Constrained Decision Trees and Coverings
arXiv:2602. 21312v4 Announce Type: replace-cross Abstract: This work considers a number of optimization problems and reductive relations between them.
arXiv Machine Learning
Aug 20
On the Slow Convergence to Trivial Solutions of Algorithms for Hard Optimization Problems
The paper investigates how optimization algorithms for hard combinatorial problems converge to trivial solutions. By combining rigorous large‑graph asymptotics with numerical experiments on maximum independent set and maximum K‑SAT, the authors show that convergence to the theoretically predicted bounds is extremely slow, especially in the intermediate regime of high constraint density. This reveals a significant gap between finite‑size performance and asymptotic expectations, indicating that practical algorithm design remains essential even when theory predicts inevitable failure.
arXiv AI
Jun 30
A binarized-domains arc-consistency algorithm for TCSPs: its computational analysis and its use as a filtering procedure in solution search algorithms
arXiv:2002. 11508v3 Announce Type: replace Abstract: TCSPs (Temporal Constraint Satisfaction Problems) [Dechter et al.
arXiv Machine Learning
Sep 4
Parameterized Hardness of Zonotope Containment and Neural Network Verification
The paper proves that several decision and approximation problems for ReLU neural networks are computationally hard. For any number of layers λ≥2, deciding whether a network’s output is positive (and thus whether it is surjective) is W[ℓ−1]-hard when parameterized by the input dimension d. In particular, for two-layer networks, the related geometric problem of zonotope non‑containment is W[1]-hard in the ambient dimension, and computing or approximating the Lp‑Lipschitz constant is NP‑hard and W[ℓ−1]-hard with respect to d. The results also show that these problems remain hard when parameterized by the number of layers for constant d, implying that naive enumeration algorithms running in n^{(ℓ−1)d}·poly(N) time are essentially optimal under the Exponential Time Hypothesis.
Hugging Face Trending Papers
Aug 19
On the Slow Convergence to Trivial Solutions of Algorithms for Hard Optimization Problems
The paper investigates how algorithms for hard combinatorial optimization problems converge to trivial solutions, focusing on finite-size behavior rather than asymptotic limits. By analyzing large-graph asymptotics and running numerical experiments on problems like maximum independent set and maximum K‑SAT, the authors show that convergence to theoretically predicted bounds is surprisingly slow. In the intermediate regime of high constraint density, local algorithms actually outperform their asymptotic predictions, highlighting a gap between finite-regime performance and asymptotic theory.
arXiv AI
Jun 26
Geometry-Aware MCTS for Extremal Problems in Combinatorial Geometry
arXiv:2606. 26399v1 Announce Type: new Abstract: We study certain extremal problems in combinatorial geometry that ask about configurations of points in an $n \times n$ grid that satisfy strict, global geometric constraints.
Hugging Face Trending Papers
Jun 24
Geometry-Aware MCTS for Extremal Problems in Combinatorial Geometry
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Hugging Face Trending Papers
Sep 9
A Sharp Barrier for Consistent Submodular Maximization: Any Improvement over $2-\sqrt{2}$ Entails Exponential Queries or Linear Recourse
Consistent submodular maximization studies the tradeoff between solution quality and stability when elements arrive over time. For a monotone submodular objective, which models diminishing returns, an...
arXiv AI
Jul 24
Representative Sets in Propositional Abduction
arXiv:2607. 21183v1 Announce Type: cross Abstract: The propositional abduction problem is a well-known form of non-monotonic reasoning where we are asked to find an explanation of a given manifestation.
arXiv Machine Learning
Sep 24
Binary Quantized Neural Network Training Is W[1]-Hard Parameterized by Input and Output Dimensions
The paper proves that training a binary quantized neural network (2-QNNT) is W[1]-hard when parameterized solely by the sum of input and output dimensions, α+ω. This hardness result holds even for zero training error on a specially constructed dataset where each input equals its target and the examples form a coordinate‑wise prefix chain. The proof reduces from DAG edge‑disjoint paths, employing a one‑flip routing equivalence that links activation transitions to vertex‑disjoint paths in the network.
arXiv AI
Aug 18
Translating finite-domain integer constraint models to CP/SMT/ILP/PB/SAT solvers with CPMpy
arXiv:2608. 15143v1 Announce Type: new Abstract: Constraint solving is a declarative approach for solving combinatorial satisfaction and optimization problems.