Computational Problem Solving (CPS) is a compulsory subject of the 5-year double Bachelor degree in Business Administration & Artificial Intelligence for Businesses of the ESADE Ramon Llull University.
This course aims to develop a comprehensive understanding of optimization and modeling approaches to effectively tackle complex economic, social, and environmental challenges. By integrating optimization methods and simulations, the course bridges the gap between theory and practice, empowering students with computational problem-solving skills. Optimization methods play a crucial role in enhancing decision-making processes and maximizing outcomes. By exploring various techniques such as hill-climbing search, simulated annealing, and tabu search. The hill-climbing search involves iteratively improving a solution by making small changes, while simulated annealing mimics the process of annealing to find global optima. Tabu search utilizes a memory mechanism to avoid revisiting previously explored solutions. Combined, these methods will equip students with tools to identify the most optimal solution, leading to increased efficiency, cost savings, and competitive advantage.
The second part of the course will delve into simulations, starting with Monte Carlo simulations that utilize random sampling to estimate outcomes. We will then introduce evolutionary algorithms that involve iterative selection, recombination, and mutation to find optimal solutions. Finally, we describe ant algorithms, a kind of algorithm that is inspired by the behavior of ants and their pheromone trails to solve complex problems. The course will end by explaining the basis of Bayesian optimization, which allows students to explore emergent properties and simulate the behavior of the complex interactions within a system, providing insights into the dynamics of environmental, and economic phenomena.
Access to the subject web : Computational Problem Solving