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Published in:
2017 | OriginalPaper | Chapter
Approximation and Complexity
Authors : Panos M. Pardalos, Antanas Žilinskas, Julius Žilinskas
Published in: Non-Convex Multi-Objective Optimization
Publisher: Springer International Publishing
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In computer science, the theory of computational complexity was developed to evaluate the hardness of problems, regarding the number of steps to obtain a solution; for the basic results of that theory we refer to [147]. The model of computations, used in that theory, is either the Turing machine or any other equivalent model. The input and output of the Turing machine should be encoded as finite strings of bits. Such an encoding can be used to represent the objects of discrete nature, and the theory of computational complexity is favorable, e.g., to the investigation of problems of single-objective combinatorial optimization [150, 151]. However, the Turing machine model is not suitable for the algorithms using real numbers. Therefore alternative complexity theories have been developed for the investigation of problems of continuous nature. For the fundamentals of the complexity of real number algorithms we refer to [19, 218], and for the complexity of problems of mathematical programming to [143, 150, 151].