Departament d'Informàtica, Matemàtica Aplicada i Estadística

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El Departament d'IMAE està format pel professorat de les àrees de coneixement de Llenguatges i Sistemes Informàtics (LSI), Matemàtica Aplicada (MA) i d’Estadística i Investigació Operativa (EIO). Es tracta d’un departament interdisciplinari amb docència a quatre centres de la UdG: Escola Politècnica Superior, Facultat de Ciències, Facultat de Medicina i Facultat d’Infermeria.
​Docència i investigació
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Jerarquia  ​
Collections in this community
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Resultats col·leccions

Cap resultat
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Opening: CP 2024. The 30th International Conference on Principles and Practice of Constraint Programming

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Learning Lagrangian Multipliers for the Travelling Salesman Problem

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Structure-guided local improvement for maximum satisfiability

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Modeling and solving combinatorial optimization problems with Domain-Independent Dynamic Programming

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Solving Patience and Solitaire Games with Good Old Fashioned AI

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Learning Effect and Compound Activities in High Multiplicity RCPSP: Application to Satellite Production

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Certifying without loss of generality reasoning in solution-improving maximum satisfiability

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Slide&Drill, a new Approach for Multi-Objective Combinatorial Optimization

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Early Career Award. Enhancing constraint programming with machine learning: current challenges and future opportunities

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Doctoral Research Award. Scalability in Decision-Focused Learning: State of the Art, Challenges, and Beyond

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Constraint Modelling with LLMs using In-Context Learning

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An Efficient Local Search Solver for Mixed Integer Programming

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Combining Constraint Programming Reasoning with Large Language Model Predictions

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Frugal Algorithm Selection

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Efficient Implementation of the Global Cardinality Constraint with Costs

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Cumulative Scheduling with Calendars and Overtime

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Minizinc Challenge Results

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Distinguished Service Award

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30th Anniversary Panel

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Deep Cooperation of Local Search and Unit Propagation Techniques

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Thinking fast and slow in AI: A cognitive architecture to augment both AI and human reasoning

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Constraint Programming with JuMP

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Exponential steepest ascent from valued constraint graphs of pathwidth four

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Constraint Acquisition - A Tutorial on Learning Constraint Models

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