arXiv subjects

Cristian Grozea

Publications and source records attributed to Cristian Grozea.

6 recordsLinked to original sources

arXiv2026

ASPIC: Proof-of-Concept ASP to Picat Transpiler

This article presents ASPIC, a new proof-of-concept library that converts extended syntax ASP-Core-2 programs to Picat predicates that can be solved right away with the integrated Picat SAT solver, or embedded in larger Picat programs ("ASP in Picat"), and that can in turn make use of various Picat predicates and functions ("Picat in ASP"). The first tests prove good compatibility with clingo, on programs lacking positive loops and when the special Picat features are not used. With the embedded Picat, it touches the application field of clingcon as well, by being able to efficiently model with both ASP atoms and with finite domain variables, but goes beyond that by being able to model also non-linear constraints.

cs.LO↗

arXiv2025

Picat Through the Lens of Advent of Code

Picat is a logic-based, multi-paradigm programming language that integrates features from logic, functional, constraint, and imperative programming paradigms. This paper presents solutions to several problems from the 2024 Advent of Code (AoC). While AoC problems are not designed for any specific programming language, certain problem types, such as reverse engineering and path-finding, are particularly well-suited to Picat due to its built-in constraint solving, pattern matching, backtracking, and dynamic programming with tabling. This paper demonstrates that Picat's features, especially its SAT-based constraint solving and tabling, enable concise, declarative, and highly efficient implementations of problems that would require significantly more effort in imperative languages.

cs.PL↗

arXiv2023

Optimising Rolling Stock Planning including Maintenance with Constraint Programming and Quantum Annealing

We propose and compare Constraint Programming (CP) and Quantum Annealing (QA) approaches for rolling stock assignment optimisation considering necessary maintenance tasks. In the CP approach, we model the problem with an Alldifferent constraint, extensions of the Element constraint, and logical implications, among others. For the QA approach, we develop a quadratic unconstrained binary optimisation (QUBO) model. For evaluation, we use data sets based on real data from Deutsche Bahn and run the QA approach on real quantum computers from D-Wave. Classical computers are used to evaluate the CP approach as well as tabu search for the QUBO model. At the current development stage of the physical quantum annealers, we find that both approaches tend to produce comparable results.

cs.AI↗

arXiv2023

Automatic Conversion of MiniZinc Programs to QUBO

Obtaining Quadratic Unconstrained Binary Optimisation models for various optimisation problems, in order to solve those on physical quantum computers (such as the the DWave annealers) is nowadays a lengthy and tedious process that requires one to remodel all problem variables as binary variables and squeeze the target function and the constraints into a single quadratic polynomial into these new variables. We report here on the basis of our automatic converter from MiniZinc to QUBO, which is able to process a large set of constraint optimisation and constraint satisfaction problems and turn them into equivalent QUBOs, effectively optimising the whole process.

cs.MS↗

arXiv2021

Solving the Extended Job Shop Scheduling Problem with AGVs -- Classical and Quantum Approaches

The subject of Job Scheduling Optimisation (JSO) deals with the scheduling of jobs in an organization, so that the single working steps are optimally organized regarding the postulated targets. In this paper a use case is provided which deals with a sub-aspect of JSO, the Job Shop Scheduling Problem (JSSP or JSP). As many optimization problems JSSP is NP-complete, which means the complexity increases with every node in the system exponentially. The goal of the use case is to show how to create an optimized duty rooster for certain workpieces in a flexible organized machinery, combined with an Autonomous Ground Vehicle (AGV), using Constraint Programming (CP) and Quantum Computing (QC) alternatively. The results of a classical solution based on CP and on a Quantum Annealing model are presented and discussed. All presented results have been elaborated in the research project PlanQK.

cs.AI↗

arXiv2013

Challenges in Representation Learning: A report on three machine learning contests

The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge. We describe the datasets created for these challenges and summarize the results of the competitions. We provide suggestions for organizers of future challenges and some comments on what kind of knowledge can be gained from machine learning competitions.

stat.ML↗