Order statistics and the linear assignment problem

Under mild conditions on the distribution functionF, we analyze the asymptotic behavior in expectation of the smallest order statistic, both for the case thatF is defined on (–, +) and for the case thatF is defined on (0, ). These results yield asymptotic estimates of the expected optiml value of the linear assignment problem under the assumption that the cost coefficients are independent random variables with distribution functionF.asymptotic analysis;linear assignment problem;order statistic

Order statistics and the linear assignment problem

Under mild conditions on the distribution functionF, we analyze the asymptotic behavior in expectation of the smallest order statistic, both for the case thatF is defined on (–, +) and for the case thatF is defined on (0, ). These results yield asymptotic estimates of the expected optiml value of the linear assignment problem under the assumption that the cost coefficients are independent random variables with distribution functionF

Revisiting old combinatorial beasts in the quantum age: quantum annealing versus maximal matching

This paper experimentally investigates the behavior of analog quantum computers such as commercialized by D-Wave when confronted to instances of the maximum cardinality matching problem specifically designed to be hard to solve by means of simulated annealing. We benchmark a D-Wave "Washington" (2X) with 1098 operational qubits on various sizes of such instances and observe that for all but the most trivially small of these it fails to obtain an optimal solution. Thus, our results suggests that quantum annealing, at least as implemented in a D-Wave device, falls in the same pitfalls as simulated annealing and therefore suggest that there exist polynomial-time problems that such a machine cannot solve efficiently to optimality

Asymptotic properties of the quadratic assignment problem

For the general quadratic assignment problem as well as for a planar version of this problem, we extend earlier work by Burkard and Fincke to prove that the ratio of the maximal to the minimal solution value converges to 1 almost surely. In fact, any solution value can almost surely be written asymptotically as a simple, explicitly given function of the problem size. Theoretical analysis and computational experiments reveal the convergence to be relatively fast

Order statistics and the linear assignment problem

Under mild conditions on the distribution function F, we analyze the asymptotic behavior in expectation of the smallest order statistic, both for the case that F is defined on (- \infty, + \infty) and for the case that F is defined on (0, \infty). These results yield asymptotic estimates of the expected optimal value of the linear assignment problem under the assumption that the cost coefficients are independent random variables with distribution function F