Results 71 to 80 of about 15,021 (304)

Robust satisfiability of constraint satisfaction problems [PDF]

open access: yesProceedings of the forty-fourth annual ACM symposium on Theory of computing, 2012
An algorithm for a constraint satisfaction problem is called robust if it outputs an assignment satisfying at least (1-g(e))-fraction of the constraints given a (1-e)-satisfiable instance, where g(e) -> 0 as e -> 0, $g(0)=0. Guruswami and Zhou conjectured a characterization of constraint languages for which the corresponding constraint satisfaction ...
Libor Barto, Marcin Kozik
openaire   +3 more sources

A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science

open access: yesAdvanced Engineering Materials, EarlyView.
We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann   +8 more
wiley   +1 more source

Constraint models for complex state transitions

open access: yesComputer Assisted Methods in Engineering and Science, 2022
Constraint-based scheduling is an approach for solving real-life scheduling problems by combining the generality of AI techniques with the efficiency of OR techniques. Basically, it describes a scheduling problem as a constraint satisfaction problem and
Roman Bartak
doaj  

Machine Learning‐Assisted Inverse Design of Soft and Multifunctional Hybrid Liquid Metal Composites

open access: yesAdvanced Functional Materials, EarlyView.
A machine learning framework is presented for inverse design of synthesizable multifunctional composites containing both liquid metal and solid inclusions. By integrating physics‐based modeling, data‐driven prediction, and Bayesian optimization, the approach enables intelligent design of experiments to identify optimal compositions and realize these ...
Lijun Zhou   +5 more
wiley   +1 more source

Extremal eigenvalues of local Hamiltonians [PDF]

open access: yesQuantum, 2017
We apply classical algorithms for approximately solving constraint satisfaction problems to find bounds on extremal eigenvalues of local Hamiltonians.
Aram W. Harrow, Ashley Montanaro
doaj   +1 more source

A Meta Constraint Satisfaction Optimization Problem for the Optimization of Regular Constraint Satisfaction Problems

open access: yesProceedings of the 11th International Conference on Agents and Artificial Intelligence, 2019
This paper describes a new approach on optimization of regular constraint satisfaction problems (rCSPs) using an auxiliary constraint satisfaction optimization problem (CSOP) that detects areas with a potentially high number of conflicts. The purpose of this approach is to remove conflicts by the combination of regular constraints with intersection and
Sven Löffler   +2 more
openaire   +1 more source

Is It Real Type‐II or S‐Scheme? A Three‐Phase Diagnostic Protocol for Identifying Potentially Mislabeled Heterojunction Photocatalysts

open access: yesAdvanced Functional Materials, EarlyView.
Diagnostic analysis via the heterojunction validation funnel. The funnel illustrates the hierarchical stratification of 30 reported Type‐II systems based on the three‐phase, seven‐step diagnostic framework. Complete validation through all phases is achieved by only 3.3% of systems, while 96.7% lack full mechanistic validation, revealing a pervasive ...
Ki‐Hyun Kim
wiley   +1 more source

Programación con restricciones dinámica

open access: yesIngenieria Industrial, 2009
Este trabajo presenta algoritmos de resolución de Problemas de Satisfacción de Restricciones, capaces de medir el desempeño de su proceso a través de indicadores relevantes, posibilitando su auto-ajuste.
Broderick Crawford   +2 more
doaj  

Quantified Constraint Handling Rules [PDF]

open access: yesElectronic Proceedings in Theoretical Computer Science, 2019
We shift the QCSP (Quantified Constraint Satisfaction Problems) framework to the QCHR (Quantified Constraint Handling Rules) framework by enabling dynamic binder and access to user-defined constraints.
Vincent Barichard, Igor Stéphan
doaj   +1 more source

A General Framework Based on Machine Learning for Algorithm Selection in Constraint Satisfaction Problems

open access: yesApplied Sciences, 2021
Many of the works conducted on algorithm selection strategies—methods that choose a suitable solving method for a particular problem—start from scratch since only a few investigations on reusable components of such methods are found in the literature ...
José C. Ortiz-Bayliss   +5 more
doaj   +1 more source

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