We introduce a parallel machine scheduling problem in
which the processing times of jobs are not given in advance but are
determined by a system of linear constraints. The objective is to
minimize the makespan, i.e., the maximum job completion time among
all feasible choices. This novel problem is motivated by various
real-world application scenarios. We discuss the computational
complexity and algorithms for various settings of this problem. In
particular, we show that if there is only one machine with an
arbitrary number of linear constraints, or there is an arbitrary
number of machines with no more than two linear constraints, or both
the number of machines and the number of linear constraints are
fixed constants, then the problem is polynomial-time solvable via
solving a series of linear programming problems. If both the number
of machines and the number of constraints are inputs of the problem
instance, then the problem is NP-Hard. We further propose several
approximation algorithms for the latter case.
In this paper, a brief introduction of the nonlinear filtering problems and a review of the quasi-implicit Euler method are presented. The major contribution of this paper is that we propose a nonnegativity-preserving algorithm of Yau-Yau method for solving high-dimensional nonlinear filtering problems by applying quasi-implicit Euler method with discrete sine transform. Furthermore, our algorithms are directly applicable on the compact difference schemes, so that the number of spatial points can be substantially reduced and retain the same accuracy. Numerical results indicate that the proposed algorithm is capable of solving up to six-dimensional nonlinear filtering problems efficiently and accurately.
It is well known that the nonlinear filter has important applications in military, engineering and commercial industries. In this paper, we propose efficient and accurate numerical algorithms for the realization of the Yau-Yau method for solving nonlinear filtering problems by using finite difference schemes. The Yau-Yau method reduces the nonlinear filtering problem to the initial-value problem of Kolmogorov equations. We first solve this problem by the implicit Euler method, which is stable in most cases, but costly. Then, we propose a quasi-implicit Euler method which is feasible for acceleration by fast Fourier transformations. Furthermore, we propose a superposition technique which enables us to deal with the nonlinear filtering problem in an off-time process and thus, save a large amount of computational cost. Next, we prove that the numerical solutions of Kolmogorov equations by our schemes are always nonnegative in each iteration. Consequently, our iterative process preserves the probability density functions. In addition, we prove convergence of our schemes under some mild conditions. Numerical results show that the proposed algorithms are efficient and promising.