By Vladik Kreinovich, Anatoly Lakeyev, Jiří Rohn, Patrick Kahl (auth.)
Targeted viewers • experts in numerical computations, specially in numerical optimiza tion, who're attracted to designing algorithms with automatie consequence ver ification, and who might for that reason have an interest in figuring out how normal their algorithms caIi in precept be. • Mathematicians and laptop scientists who're drawn to the idea zero/ computing and computational complexity, specially computational com plexity of numerical computations. • scholars in utilized arithmetic and laptop technology who're attracted to computational complexity of alternative numerical tools and in studying common recommendations for estimating this computational complexity. The e-book is written with all factors and definitions further, in order that it may be used as a graduate point textbook. What this e-book .is approximately facts processing. in lots of real-life events, we're attracted to the worth of a actual volume y that's diflicult (or even very unlikely) to degree without delay. for instance, it's most unlikely to at once degree the volume of oil in an oil box or a distance to a celebrity. on the grounds that we can't degree such amounts at once, we degree them in a roundabout way, by means of measuring another amounts Xi and utilizing the identified relation among y and Xi'S to reconstruct y. The set of rules that transforms the consequences Xi of measuring Xi into an estimate fj for y is termed info processing.
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Extra info for Computational Complexity and Feasibility of Data Processing and Interval Computations
So, the Turing machine-based complexity is a rather poor estimate for the actual computation time of an algorithm on a real computer. If we use more sophisticated computer models, we can get much better estimates. What makes Turing machine-based complexity standard and widely used is the fact that although the actual computation time changes from one type of computer to another, but whether the algorithm is polynomial-time or not does not depend on our choice of the computer. , Emde Boas ).
When is a Problem Tractable, and When 'ts It Intractable? 4. • A problem (not necessarily from the dass NP) is called NP-hard if every problem from the dass NP can be reduced to it. • If a problem from the dass NP is NP-hard, it is called NP-complete. If a problem P is NP-hard, then every feasible algorithm for solving this problem P would lead to feasible algorithms for solving all problems from the dass NP, and this is generally believed to be hardly possible. • For example, mathematicians believe that not only there is no algorithm for checking whether a give statement is provable or not (the famous Gödel's theorem has proven that), but also they believe that there is no feasible way to find a proof of a given statement even if we restrict the lengths of possible proofs.
Readers who do not feel comfortable with one ofthe example (say, with a physical one) are free to simply skip it. • (Example from mathematics) We are given a mathematical statement x. , a proof of "not x"). Here, R(x, y) means that y is a proof either of x, or of "not x". 34 • CHAPTER 2 (Example from physics) xis the results of the experiments, and the desired y is the formula that fits all these data. 0), ... , y is the formula X2 = 2 . xt). Solution must be checkable. For a problem to be practically meaningful, we must have a way to check whether the proposed solution is correct.