Primality Testing in Polynomial Time From Randomized Algorithms to "PRIMES Is in P" /
Dietzfelbinger, Martin.
Primality Testing in Polynomial Time From Randomized Algorithms to "PRIMES Is in P" / [electronic resource] : by Martin Dietzfelbinger. - 1st ed. 2004. - X, 150 p. online resource. - Lecture Notes in Computer Science, 3000 1611-3349 ; . - Lecture Notes in Computer Science, 3000 .
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.
9783540259336
10.1007/b12334 doi
Number theory.
Algebra.
Algorithms.
Computer science.
Cryptography.
Data encryption (Computer science).
Computer science--Mathematics.
Mathematical statistics.
Number Theory.
Algebra.
Algorithms.
Theory of Computation.
Cryptology.
Probability and Statistics in Computer Science.
QA241-247.5
512.7
Primality Testing in Polynomial Time From Randomized Algorithms to "PRIMES Is in P" / [electronic resource] : by Martin Dietzfelbinger. - 1st ed. 2004. - X, 150 p. online resource. - Lecture Notes in Computer Science, 3000 1611-3349 ; . - Lecture Notes in Computer Science, 3000 .
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.
9783540259336
10.1007/b12334 doi
Number theory.
Algebra.
Algorithms.
Computer science.
Cryptography.
Data encryption (Computer science).
Computer science--Mathematics.
Mathematical statistics.
Number Theory.
Algebra.
Algorithms.
Theory of Computation.
Cryptology.
Probability and Statistics in Computer Science.
QA241-247.5
512.7