Primality Testing in Polynomial Time [electronic resource] : From Randomized Algorithms to "PRIMES Is in P" / by Martin Dietzfelbinger.

By: Dietzfelbinger, Martin [author.]Contributor(s): SpringerLink (Online service)Material type: TextTextLanguage: English Series: Lecture Notes in Computer Science: 3000Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2004Description: X, 150 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540259336Subject(s): Computer science | Data encryption (Computer science) | Computer software | Algorithms | Number theory | Computer Science | Algorithm Analysis and Problem Complexity | Computation by Abstract Devices | Data Encryption | Probability and Statistics in Computer Science | Number Theory | AlgorithmsAdditional physical formats: Printed edition:: No titleDDC classification: 005.1 LOC classification: QA76.9.A43Online resources: Click here to access online
Contents:
1. Introduction: Efficient Primality Testing -- 2. Algorithms for Numbers and Their Complexity -- 3. Fundamentals from Number Theory -- 4. Basics from Algebra: Groups, Rings, and Fields -- 5. The Miller-Rabin Test -- 6. The Solovay-Strassen Test -- 7. More Algebra: Polynomials and Fields -- 8. Deterministic Primality Testing in Polynomial Time -- A. Appendix.
In: Springer eBooksSummary: On August 6, 2002,a paper with the title “PRIMES is in P”, by M. Agrawal, N. Kayal, and N. Saxena, appeared on the website of the Indian Institute of Technology at Kanpur, India. In this paper it was shown that the “primality problem”hasa“deterministic algorithm” that runs in “polynomial time”. Finding out whether a given number n is a prime or not is a problem that was formulated in ancient times, and has caught the interest of mathema- ciansagainandagainfor centuries. Onlyinthe 20thcentury,with theadvent of cryptographic systems that actually used large prime numbers, did it turn out to be of practical importance to be able to distinguish prime numbers and composite numbers of signi?cant size. Readily, algorithms were provided that solved the problem very e?ciently and satisfactorily for all practical purposes, and provably enjoyed a time bound polynomial in the number of digits needed to write down the input number n. The only drawback of these algorithms is that they use “randomization” — that means the computer that carries out the algorithm performs random experiments, and there is a slight chance that the outcome might be wrong, or that the running time might not be polynomial. To ?nd an algorithmthat gets by without rand- ness, solves the problem error-free, and has polynomial running time had been an eminent open problem in complexity theory for decades when the paper by Agrawal, Kayal, and Saxena hit the web.
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1. Introduction: Efficient Primality Testing -- 2. Algorithms for Numbers and Their Complexity -- 3. Fundamentals from Number Theory -- 4. Basics from Algebra: Groups, Rings, and Fields -- 5. The Miller-Rabin Test -- 6. The Solovay-Strassen Test -- 7. More Algebra: Polynomials and Fields -- 8. Deterministic Primality Testing in Polynomial Time -- A. Appendix.

On August 6, 2002,a paper with the title “PRIMES is in P”, by M. Agrawal, N. Kayal, and N. Saxena, appeared on the website of the Indian Institute of Technology at Kanpur, India. In this paper it was shown that the “primality problem”hasa“deterministic algorithm” that runs in “polynomial time”. Finding out whether a given number n is a prime or not is a problem that was formulated in ancient times, and has caught the interest of mathema- ciansagainandagainfor centuries. Onlyinthe 20thcentury,with theadvent of cryptographic systems that actually used large prime numbers, did it turn out to be of practical importance to be able to distinguish prime numbers and composite numbers of signi?cant size. Readily, algorithms were provided that solved the problem very e?ciently and satisfactorily for all practical purposes, and provably enjoyed a time bound polynomial in the number of digits needed to write down the input number n. The only drawback of these algorithms is that they use “randomization” — that means the computer that carries out the algorithm performs random experiments, and there is a slight chance that the outcome might be wrong, or that the running time might not be polynomial. To ?nd an algorithmthat gets by without rand- ness, solves the problem error-free, and has polynomial running time had been an eminent open problem in complexity theory for decades when the paper by Agrawal, Kayal, and Saxena hit the web.

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