exponential-time algorithm - significado y definición. Qué es exponential-time algorithm
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Qué (quién) es exponential-time algorithm - definición

ESTIMATE OF TIME TAKEN FOR RUNNING AN ALGORITHM
Polynomial time; Exponential time; Linearithmic function; Subquadratic time; Running time; Linear time; Cubic time; Quadratic time; Algorithmic time complexity; Polynomial-time; Polynomial-time algorithm; Polynomial-time solutions; Polynomial-time solution; Computation time; Constant time; Exponential algorithm; Logarithmic time; Linear-time; Linearithmic; N log n; Weakly polynomial; Strongly polynomial; Run-time complexity; Sublinear time; Sublinear-time; Sublinear time algorithm; Linearithm; Computational time; Sub-exponential time; Super-polynomial time; Superpolynomial; Fast algorithms; Quasi-polynomial time; SUBEXP; Linearithmic time; Double exponential time; Polylogarithmic time; Sub-linear time; Polynomial time algorithm; Subexponential time; Nlogn; Quasilinear time; Strongly polynomial time; Polynomial complexity; Linear-time algorithm; Linear time agorithm; Sublinear algorithm; Polytime; Weakly polynomial time algorithm; Time complexities

exponential-time algorithm      
<complexity> An algorithm (or Turing Machine) that is guaranteed to terminate within a number of steps which is a exponential function of the size of the problem. For example, if you have to check every number of n digits to find a solution, the complexity is O(10^n), and if you add an extra digit, you must check ten times as many numbers. Even if such an algorithm is practical for some given value of n, it is likely to become impractical for larger values. This is in contrast to a polynomial-time algorithm which grows more slowly. See also computational complexity, polynomial-time, NP-complete. (1995-04-27)
exponential function         
  • The red curve is the exponential function.  The black horizontal lines show where it crosses the green vertical lines.
  • The exponential function e^z plotted in the complex plane from -2-2i to 2+2i
MATHEMATICAL FUNCTION WITH A CONSTANT BASE AND A VARIABLE EXPONENT, DENOTED EXP_A(X) OR A^X
Complex exponential function; Complex exponential; Natural exponential function; E^x; Exp(x); Exp (programming); Complex exponentials; Real exponential function; E**x; E to the x; Cb^x; Exponential Function; Exponential equation; Exponential equations; ⅇ; Natural exponent; Exponential minus 1 function; Exponential minus 1; Expm1; Exp-1; Exp1m; Expm1(x); Exp1m(x); Natural exponential minus 1; Natural exponential; E^X-1; E^x-1; Exp(x)-1; Base e antilogarithm; Exponent of e; Base e anti-logarithm; Exponential minus one function; Exponential minus one; Natural exponential minus one; Natural exponential minus one function; Exponential near 0; Exponential near zero; Natural exponential near 0; Natural exponential near zero; Eˣ-1; Eˣ - 1; Eˣ; Eˣ−1; Eˣ − 1; E^x−1; Exp(x)−1; Exponential base
¦ noun Mathematics a function whose value is a constant raised to the power of the argument, especially the function where the constant is e.
Exponential time hypothesis         
UNPROVEN COMPUTATIONAL HARDNESS ASSUMPTION THAT 3-SAT ISN’T SOLVABLE IN SUBEXPONENTIAL TIME IN THE WORST CASE
ETH (complexity); Strong exponential time hypothesis
In computational complexity theory, the exponential time hypothesis is an unproven computational hardness assumption that was formulated by . The hypothesis states that 3-SAT cannot be solved in subexponential time in the worst case.

Wikipedia

Time complexity

In computer science, the time complexity is the computational complexity that describes the amount of computer time it takes to run an algorithm. Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, supposing that each elementary operation takes a fixed amount of time to perform. Thus, the amount of time taken and the number of elementary operations performed by the algorithm are taken to be related by a constant factor.

Since an algorithm's running time may vary among different inputs of the same size, one commonly considers the worst-case time complexity, which is the maximum amount of time required for inputs of a given size. Less common, and usually specified explicitly, is the average-case complexity, which is the average of the time taken on inputs of a given size (this makes sense because there are only a finite number of possible inputs of a given size). In both cases, the time complexity is generally expressed as a function of the size of the input.: 226  Since this function is generally difficult to compute exactly, and the running time for small inputs is usually not consequential, one commonly focuses on the behavior of the complexity when the input size increases—that is, the asymptotic behavior of the complexity. Therefore, the time complexity is commonly expressed using big O notation, typically O ( n ) {\displaystyle O(n)} , O ( n log n ) {\displaystyle O(n\log n)} , O ( n α ) {\displaystyle O(n^{\alpha })} , O ( 2 n ) {\displaystyle O(2^{n})} , etc., where n is the size in units of bits needed to represent the input.

Algorithmic complexities are classified according to the type of function appearing in the big O notation. For example, an algorithm with time complexity O ( n ) {\displaystyle O(n)} is a linear time algorithm and an algorithm with time complexity O ( n α ) {\displaystyle O(n^{\alpha })} for some constant α > 1 {\displaystyle \alpha >1} is a polynomial time algorithm.