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The routine witness,shown in Figure 108 computes A1(modP), augmented to return 0 if a violation of Theorem 102 is detected If witness does not return 1, then A is a witness to the fact that N cannot be prime Lines 14 through 16 make a recursive call and produce X We then compute X ' , as is normal for the power computation We check whether Theorem 102 is violated, returning 0 if it is Otherwise, we complete the power computation The only remaining issue is correctness If our algorithm declares that N is composite, then N must be composite If N is composite, are all A 2 I I N - 2 witnesses The answer, unfortunately, is no That is, some choices of A will trick our algorithm into declaring that N is prime In fact, if we choose A randomly, we have at most a 114 chance of failing to detect a composite number and thus making an error Note that this outcome is true for any N If it were obtained only by averaging over all N, we would not have a good enough routine Analogous to medical tests, our algorithm generates false positives at most 25 percent of the time for any N These odds do not seem very good because a 25 percent error rate generally is considered very high However, if we independently use 20 values of A, the chances that none of them will witness a composite number is 1/420, which is about one in a million million Those odds are much more reasonable and can be made even better by using more trials The routine isprime,which is also shown in Figure 108, uses five triak3

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In this chapter we described how random numbers are generated and used The linear congruential generator is a good choice for simple applications, so long as care is taken in choosing the parameters A and M Using a uniform random number generator, we can derive random numbers for other distributions, such as the Poisson and negative exponential distributions Random numbers have many uses, including the empirical study of algorithms, the simulation of real-life systems, and the design of algorithms that probabilistically avoid the worst case We use random numbers in other parts of this text, notably in Section 142 and Exercise 2120 1 This concludes Part 1 of the book In Part 111 we look at some simple applications, beginning with a discussion of games in I 1 that illustrates three important problem-solving techniques

elevated temperatures are unknown. NCDOT and I agreed that some sort of mechanical testing would be required to verify that each piece had the necessary integrity for its role in the bridge. It was agreed that rst all timbers should be graded in accordance with SPIB Grading Rules. Drilled holes would be considered to be open knots with the same criteria applied regarding size, location, and frequency. After grading, all suitable timbers would be load tested by applying a known load and measuring the de ection across a given span in order to compute the modulus of elasticity. This would provide a measure of stiffness for each piece, presumably enabling us to discard pieces that fell below the published values for the grade. Certainly, knowing the modulus of elasticity for compressive members is particularly valuable. For tension members, it was thought that the recycled material, being extremely dense, would be at least as strong as published values for the same grade of new material.

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Probabilistic primality testing routine If witness does not return 1 , n is definitely composite Do this by computing aAi ( mod n ) and looking for non-trivial square roots of 1 along the way HugeInt: must have copy constructor, operator=, conversion from int, * , / , - , 4 , ==, and ! = template <class HugeInt> HugeInt witness( const HugeInt & a, const HugeInt & i, const HugeInt & n )

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