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SSP can also be regarded as an optimization problem: find a subset whose sum is at most T, and subject to that, as close as possible to T. It is NP-hard, but there are several algorithms that can solve it reasonably quickly in practice. SSP is a special case of the knapsack problem and of the multiple subset sum problem.
This function itself must be synthesized in the process of SSP. In this case, realization of the axiom is a higher order function, i.e., a function that uses another function as an input. For instance, the formula (state → nextState) ∧ initialState → result. can specify a higher order function with two inputs and an output result.
The DSSP algorithm is the standard method for assigning secondary structure to the amino acids of a protein, given the atomic-resolution coordinates of the protein. The abbreviation is only mentioned once in the 1983 paper describing this algorithm, [2] where it is the name of the Pascal program that implements the algorithm Define Secondary Structure of Proteins.
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The tests are the monobit test (equal numbers of ones and zeros in the sequence), poker test (a special instance of the chi-squared test), runs test (counts the frequency of runs of various lengths), longruns test (checks whether there exists any run of length 34 or greater in 20 000 bits of the sequence)—both from BSI [22] and NIST, [23] and ...
python-sgp4 A Python Implementation of the sgp4 model with automatic downloading of TLE Elements from NORAD database. PHP5 based on Gpredict; Java: SDP4 and predict4java; C++, FORTRAN, Pascal, and MATLAB. go-satellite GoLang implementation of SGP4 model and helper utilities.
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Algorithms for calculating variance play a major role in computational statistics.A key difficulty in the design of good algorithms for this problem is that formulas for the variance may involve sums of squares, which can lead to numerical instability as well as to arithmetic overflow when dealing with large values.