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Introduced by Jack Dongarra, they measure how fast a computer solves a dense n × n system of linear equations Ax = b, which is a common task in engineering. The latest version of these benchmarks is used to build the TOP500 list, ranking the world's most powerful supercomputers. [1]
In this decryption example, the ciphertext that will be decrypted is the ciphertext from the encryption example. The corresponding decryption function is D(y) = 21(y − b) mod 26, where a −1 is calculated to be 21, and b is 8. To begin, write the numeric equivalents to each letter in the ciphertext, as shown in the table below.
Condition numbers can also be defined for nonlinear functions, and can be computed using calculus.The condition number varies with the point; in some cases one can use the maximum (or supremum) condition number over the domain of the function or domain of the question as an overall condition number, while in other cases the condition number at a particular point is of more interest.
ax + by = c. where a, b and c are given integers. This can be written as an equation for x in modular arithmetic: ax ≡ c mod b. Let g be the greatest common divisor of a and b. Both terms in ax + by are divisible by g; therefore, c must also be divisible by g, or the equation has no solutions.
The conjugate gradient method can be applied to an arbitrary n-by-m matrix by applying it to normal equations A T A and right-hand side vector A T b, since A T A is a symmetric positive-semidefinite matrix for any A. The result is conjugate gradient on the normal equations (CGN or CGNR). A T Ax = A T b
If d is the greatest common divisor of a and m then the linear congruence ax ≡ b (mod m) has solutions if and only if d divides b. If d divides b, then there are exactly d solutions. [7] A modular multiplicative inverse of an integer a with respect to the modulus m is a solution of the linear congruence ().
In mathematical optimization, the Karush–Kuhn–Tucker (KKT) conditions, also known as the Kuhn–Tucker conditions, are first derivative tests (sometimes called first-order necessary conditions) for a solution in nonlinear programming to be optimal, provided that some regularity conditions are satisfied.
algorithm Gauss–Seidel method is inputs: A, b output: φ Choose an initial guess φ to the solution repeat until convergence for i from 1 until n do σ ← 0 for j from 1 until n do if j ≠ i then σ ← σ + a ij φ j end if end (j-loop) φ i ← (b i − σ) / a ii end (i-loop) check if convergence is reached end (repeat)