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All entries for Tuesday 26 December 2006

## December 26, 2006

### pseudo–polynomial Vs. Strongly polynomial.

In algorithmic complexity, two other terms are heard frequently: strongly polynomial and pseudo-polynomial. A strongly polynomial-time algorithm is one whose running time is bounded polynomially by a function only of the inherent dimensions of the problem and independent of the sizes of the numerical data. For example, most sorting algorithms are strongly polynomial, since they normally require a number of comparisons polynomial in the number of entries and do not depend on the actual values being sorted; an algorithm for a network problem would be strongly polynomial if its running time depended only on the numbers of nodes and arcs in the network, and not on the sizes of the costs or capacities.

A pseudo-polynomial-time algorithm is one that runs in time polynomial in the dimension of the problem and the magnitudes of the data involved (provided these are given as integers), rather than the base-two logarithms of their magnitudes. Such algorithms are technically exponential functions of their input size and are therefore not considered polynomial. Indeed, some NP-complete and NP-hard problems are pseudo-polynomially solvable (sometimes these are called weakly NP-hard or-complete, or NP-complete in the ordinary sense). For example, the NP-hard knapsack problem can be solved by a dynamic programming algorithm requiring a number of steps polynomial in the size of the knapsack and the number of items (assuming that all data are scaled to be integers). This algorithm is exponential-time since the input sizes of the objects and knapsack are logarithmic in their magnitudes. However, as Garey and Johnson (1979) observe, “A pseudo-polynomial-time algorithm … will display ‘exponential behavior’ only when confronted with instances containing ‘exponentially large’ numbers, [which] might be rare for the application we are interested in. If so, this type of algorithm might serve our purposes almost as well as a polynomial time algorithm.” The related term strongly NP-complete (or unary NP-complete) refers to those problems that remain NP-complete even if the data are encoded in unary (that is, if the data are “small” relative to the overall input size). Consequently, if a problem is strongly NP-complete then it cannot have a pseudo-polynomial-time algorithm unless P = NP.

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