Computation methods refer to various approaches and techniques used to solve problems or perform calculations in mathematics, science, and engineering. These methods often involve algorithms, mathematical models, and computational tools to analyze data, simulate systems, or optimize processes.
- Numerical methods: Used to approximate solutions to mathematical problems that can’t be solved analytically, such as:
- Finite difference methods
- Finite element methods
- Monte Carlo methods
- Optimization techniques: Used to find the best solution among many possibilities, including:
- Linear programming
- Nonlinear programming
- Genetic algorithms
- Statistical methods: Used for data analysis and inference, such as:
- Regression analysis
- Time series analysis
- Bayesian inference
- Machine learning algorithms: Used for pattern recognition and prediction, including:
- Neural networks
- Support vector machines
- Decision trees
- Differential equation solvers: Used to solve complex systems of equations, such as:
- Runge-Kutta methods
- Euler methods
- Predictor-corrector methods
- Symbolic computation: Used for manipulating mathematical expressions and equations symbolically.
- Parallel computing: Techniques for distributing computations across multiple processors or computers.
These methods are applied across various fields, including physics, engineering, economics, and computer science, to solve complex problems that would be difficult or impossible to address analytically.
Learning
- Computation Methods for Economists by Jesús Fernández-Villaverde at UPenn, for a good overview of the field.
- Computational Methods for Economists using Python by Richard W. Evans, PhD.
- jdebacker/CompEcon_Fall21: ECON 833: Computational Methods for Economists