We discuss the global dynamic load balancing problem of parallel MIMD architectures with arbitrary topologies. The features of computational algorithms which must be taken into account applying our approach are indica...
详细信息
We discuss the global dynamic load balancing problem of parallel MIMD architectures with arbitrary topologies. The features of computational algorithms which must be taken into account applying our approach are indicated. The central part of this method is a linear optimization problem (linearprogramming). It is well known that for resolving such problems one has the polynomial algorithms discovered by Hachijan in 1979. Hence, we describe the exact polynomial load balancing algorithm for some computing models. A sample of applying our approach for an adaptive fluid dynamics algorithm is presented.
We develop an efficient homogeneous and self-dual interior-point method (IPM) for the linear programs arising in economic model predictive control of constrained linear systems with linear objective functions. The alg...
详细信息
We develop an efficient homogeneous and self-dual interior-point method (IPM) for the linear programs arising in economic model predictive control of constrained linear systems with linear objective functions. The algorithm is based on a Riccati iteration procedure, which is adapted to the linear system of equations solved in homogeneous and self-dual IPMs. Fast convergence is further achieved using a warm-start strategy. We implement the algorithm in MATLAB and C. Its performance is tested using a conceptual power management case study. Closed loop simulations show that: 1) the proposed algorithm is significantly faster than several state-of-the-art IPMs based on sparse linear algebra and 2) warm-start reduces the average number of iterations by 35%-40%.
De Ghellinck and Vial [2] developed a single-phase polynomial projective method for the primal linearprogramming problem. The linear lower-bound update that they give does not ensure convergence with non-feasible ite...
详细信息
De Ghellinck and Vial [2] developed a single-phase polynomial projective method for the primal linearprogramming problem. The linear lower-bound update that they give does not ensure convergence with non-feasible iterates, and is therefore supplemented by a quadratic procedure. In this paper, linear updates are derived that guarantee convergence of the algorithm in a polynomial number of steps. In particular, it is shown that it suffices to add only a single extra variable to the standard linear update for feasible iterates in order to achieve this result.
Semi-infinite linear programs often arise as the limit of a sequence of approximating linear programs. Hence, studying the behavior of extensions of linear programming algorithms to semi-infinite problems can yield va...
详细信息
Semi-infinite linear programs often arise as the limit of a sequence of approximating linear programs. Hence, studying the behavior of extensions of linear programming algorithms to semi-infinite problems can yield valuable insight into the behavior of the underlying linearprogramming algorithm when the number of constraints or the number of variables is very large. In this paper, we study the behavior of the affine-scaling algorithm on a particular semi-infinite linearprogramming problem. We show that the continuous trajectories converge to the optimal solution but that, for any strictly positive step, there are starting points for which the discrete algorithm converges to nonoptimal boundary points.
Tsuchiya and Muramatsu recently proved that the affine-scaling algorithm for linearprogramming generates convergent sequences of primal and dual variables whose limits are optimal for the corresponding primal and dua...
详细信息
Tsuchiya and Muramatsu recently proved that the affine-scaling algorithm for linearprogramming generates convergent sequences of primal and dual variables whose limits are optimal for the corresponding primal and dual problems as long as the step size is no more than two-thirds of the distance to the nearest face of the polytope. An important feature of this result is that it does not require any nondegeneracy assumptions. In this paper we show that Tsuchiya and Muramatsu's result is sharp by providing a simple linearprogramming problem for which the sequence of dual variables fails to converge for every step size greater than two-thirds.
Estimating agent's skill ratings from team competition results has many applications in the real world. Existing models assume skills are the same for all contexts. However, skills are context-sensitive in a varie...
详细信息
ISBN:
(纸本)9780769542638
Estimating agent's skill ratings from team competition results has many applications in the real world. Existing models assume skills are the same for all contexts. However, skills are context-sensitive in a variety of cases. In this paper, we present a Factor-Based Context-Sensitive Skill Rating System(FBCS-SRS). Instead of estimating agent skills under every context, which is hard due to data sparisity, we propose a factor model where individual skills are modelled by the inner product of an agent factor vector and a context factor vector. Collapsed Gibbs sampling is used for approximate inference. We formulate the problem of sampling linear constraint factors as a variant of MAX-SAT, and solve it by linear programming algorithms. We validate our model on two real-world datasets. Experiments show that FBCS-SRS achieves significantly higher prediction accuracy than other skill rating systems. The improvement is even more obvious when there are a lot of contexts.
暂无评论