Category: Computation Models and Computational Complexity

Computer scientists use mathematical measures of complexity that allow them to predict, before writing the code, how fast an algorithm will run and how much memory it will require. Such predictions are important guides for programmers implementing and selecting algorithms for real-world applications.

Exploring Types of Computational Thinking Techniques. The core of Computational Thinking revolves around four integral techniques: Decomposition, Pattern Recognition, Abstraction, and Algorithmic Thinking. These techniques interplay with each other, creating a cohesive system to solve problems.

Simple model of computation. Input on tape is a finite string with symbols from a finite alphabet. Finite number of states. State transitions determined by current state and input symbol.

Examples of common computational models are weather forecasting models, earth simulator models, flight simulator models, molecular protein folding models, Computational Engineering Models (CEM), and neural network models.

The field is divided into three major branches: automata theory, computability theory and computational complexity theory. In order to perform a rigorous study of computation, computer scientists work with a mathematical abstraction of computers called a model of computation.

Computational modeling is the use of computers to simulate and study complex systems using mathematics, physics and computer science. A computational model contains numerous variables that characterize the system being studied.

The type of computational problem: The most commonly used problems are decision problems. However, complexity classes can be defined based on function problems, counting problems, optimization problems, promise problems, etc.

In computer science, the computational complexity or simply complexity of an algorithm is the amount of resources required to run it. Particular focus is given to computation time (generally measured by the number of needed elementary operations) and memory storage requirements.

Many computational models have been invented. Some of the most important are the deterministic finite automaton, the nondeterministic finite automaton, the deterministic pushdown automaton, the nondeterministic pushdown automaton, the deterministic Turing machine, and the nondeterministic Turing machine.

Computational complexity theory has shown that the set of problems that are solvable fall into different complexity classes. Most fundamentally, a problem can be considered efficiently solvable if it requires no more than a polynomial number of steps, even in worst-case scenarios.