The project ‘nxnes’ (NxN Equalisations Solver) is the implementation of project 5 from the list of selected projects in the course “Operating Systems II – Laboratory” (Department of Informatics and Communications, T.E.I. of Central Macedonia). The objective of this project was the implementation of the Gauss Elimination Method to solve a NxN system of linear equations.
Category: Numerical Methods
Taxonomy
Simulated Annealing is a global optimization algorithm that belongs to the field of Stochastic Optimization and Metaheuristics. Simulated Annealing is an adaptation of the Metropolis-Hastings Monte Carlo algorithm and is used in function optimization. Like the Genetic Algorithm, it provides a basis for a large variety of extensions and specialization’s of the general method not limited to Parallel Simulated Annealing, Fast Simulated Annealing, and Adaptive Simulated Annealing.
Taxonomy
Iterated Local Search is a Metaheuristic and a Global Optimization technique. It is an extension of Multi Start Search and may be considered a parent of many two-phase search approaches such as the Greedy Randomized Adaptive Search Procedure and Variable Neighborhood Search.
Taxonomy
Random search belongs to the fields of Stochastic Optimization and Global Optimization. Random search is a direct search method as it does not require derivatives to search a continuous domain. This base approach is related to techniques that provide small improvements such as Directed Random Search, and Adaptive Random Search.
Taxonomy
The Adaptive Random Search algorithm belongs to the general set of approaches known as Stochastic Optimization and Global Optimization. It is a direct search method in that it does not require derivatives to navigate the search space. Adaptive Random Search is an extension of the Random Search and Localized Random Search algorithms.
Taxonomy
The Stochastic Hill Climbing algorithm is a Stochastic Optimization algorithm and is a Local Optimization algorithm (contrasted to Global Optimization). It is a direct search technique, as it does not require derivatives of the search space. Stochastic Hill Climbing is an extension of deterministic hill climbing algorithms such as Simple Hill Climbing (first-best neighbor), Steepest-Ascent Hill Climbing (best neighbor), and a parent of approaches such as Parallel Hill Climbing and Random-Restart Hill Climbing.
Within the framework of the course “Numerical Methods in Programming Environments – Theory” (Department of Informatics and Communications, T.E.I. of Central Macedonia) we were asked to develop an optional program that implements Müller’s numerical method for finding the root of equations of the form f(x) = 0.
Efstathios Chatzikyriakidis 