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That reduction function must be a computable function. In particular, we often show that a problem P is undecidable by showing that the halting problem reduces to P. The complexity classes P, NP and PSPACE are closed under (many-one, "Karp") polynomial-time reductions. The complexity classes L, NL, P, NP and PSPACE are closed under log-space ...
Model order reduction aims to lower the computational complexity of such problems, for example, in simulations of large-scale dynamical systems and control systems. By a reduction of the model's associated state space dimension or degrees of freedom , an approximation to the original model is computed which is commonly referred to as a reduced ...
The elementary functions are constructed by composing arithmetic operations, the exponential function (), the natural logarithm (), trigonometric functions (,), and their inverses. The complexity of an elementary function is equivalent to that of its inverse, since all elementary functions are analytic and hence invertible by means of Newton's ...
In computational complexity theory, a polynomial-time reduction is a method for solving one problem using another. One shows that if a hypothetical subroutine solving the second problem exists, then the first problem can be solved by transforming or reducing it to inputs for the second problem and calling the subroutine one or more times.
However, the complexity of an algorithm may vary dramatically for different inputs of the same size. Therefore, several complexity functions are commonly used. The worst-case complexity is the maximum of the complexity over all inputs of size n, and the average-case complexity is the average of the complexity over all inputs of size n (this ...
In computability theory and computational complexity theory, a many-one reduction (also called mapping reduction [1]) is a reduction that converts instances of one decision problem (whether an instance is in ) to another decision problem (whether an instance is in ) using a computable function.
The green and blue functions both incur zero loss on the given data points. A learned model can be induced to prefer the green function, which may generalize better to more points drawn from the underlying unknown distribution, by adjusting , the weight of the regularization term.
In the standard form it is possible to assume, without loss of generality, that the objective function f is a linear function.This is because any program with a general objective can be transformed into a program with a linear objective by adding a single variable t and a single constraint, as follows: [9]: 1.4