Extensions to the repetitive branch and bound algorithm for globally optimal clusterwise regression

A branch and bound strategy is proposed for solving the clusterwise regression problem, extending Brusco's repetitive branch and bound algorithm (RBBA). The resulting strategy relies upon iterative heuristic optimization, new ways of observation sequencing, and branch and bound optimization of a limited number of ending subsets. These three key features lead to significantly faster optimization of the complete set and the strategy has more general applications than only for clusterwise regression. Additionally, an efficient implementation of incremental calculations within the branch and bound search algorithm eliminates most of the redundant ones. Experiments using both real and synthetic data compare the various features of the proposed optimization algorithm and contrasts them against a benchmark mixed logical-quadratic programming formulation optimized by CPLEX. The results indicate that all components of the proposed algorithm provide significant improvements in processing times, and, when combined, generally provide the best performance, significantly outperforming CPLEX.     

Modeling And Performance Evaluation Of Branch And Value Prediction In Ilp Processors

Speculative execution is one of the key issues to boost the performance of future generation microprocessors. In this paper, we introduce a novel approach to evaluate the effects of branch and value prediction, which allow the processor to execute instructions beyond the limits of control and true data dependences. Until now, almost all the estimations of their performance potential under different scenarios have been obtained using trace-driven or execution-driven simulation. Occasionally, some simple deterministic models have been used. We employ an analytical model based on recently introduced Fluid Stochastic Petri Nets (FSPNs) in order to capture the dynamic behavior of an ILP processor with aggressive use of prediction techniques and speculative execution. Here we define the FSPN model, derive the state equations for the underlying stochastic process and present performance evaluation results to illustrate its usage in deriving measures of interest. Our implementation-independent stochastic modeling framework reveals considerable potential for further research in this area using numerical solution of systems of partial differential equations and/or discrete-event simulation of FSPN models.     

基于有限域上正交矩阵构造最佳扩散函数

研究有限域GF(28)上线性变换对应的正交矩阵具有最佳分支数的判定问题,得到若干相关结果,由此提出了筛选分支数最佳正交矩阵的算法,并使用c语言进行了编程实现。通过从4阶方阵空间内抽取一部分进行穷举搜索,搜索2×316个方阵并一一进行构造和筛选,最终找到4784479个具有最佳分支数5的4阶正交无零元方阵。     

商业广告排版问题研究

研究对象是报纸的商业广告排版问题。将部分或全部待排商业广告指派到广告区域中，目标是广告之间不相互叠压地填满整个广告区域。首先根据问题的特点建立数学模型；然后运用两级分支定界算法对模型进行求解。仿真实验结果表明，该算法能快速地找到解，适合于实际应用。     

Robust model predictive control using tubes

A form of feedback model predictive control (MPC) that overcomes disadvantages of conventional MPC but which has manageable computational complexity is presented. The optimal control problem, solved on-line, yields a ‘tube’ and an associated piecewise affine control law that maintains the controlled trajectories in the tube despite uncertainty; computational complexity is linear (rather than exponential) in horizon length. Asymptotic stability of the controlled system is established.     

一种星形件支臂端结构的改进

某型直升机在长时间使用过程中，星形件支臂端衬套经常出现松动，这里介绍了该型直升机星形件支臂端衬套出现松动的原因，提出了结构改进措施。     

A pure proactive scheduling algorithm for multiple earth observation satellites under uncertainties of clouds

Most earth observation satellites (EOSs) are equipped with optical sensors, which cannot see through clouds. Hence, observations are significantly affected and blocked by clouds. In this work, with the inspiration of the notion of a forbidden sequence, we propose a novel assignment formulation for EOS scheduling. Considering the uncertainties of clouds, we formulate the cloud coverage for observations as stochastic events, and extend the assignment formulation to a chance constraint programming (CCP) model. To solve the problem, we suggest a sample approximation (SA) method, which transforms the CCP model into an integer linear programming (ILP) model. Subsequently, a branch and cut (B&C) algorithm based on lazy constraint generation is developed to solve the ILP model. Finally, we conduct a lot of simulation experiments to verify the effectiveness and efficiency of our proposed formulation and algorithm.     

Improving branch divergence performance on GPGPU with a new PDOM stack and multi-level warp scheduling

General-purpose graphics processing unit (GPGPU) plays an important role in massive parallel computing nowadays. A GPGPU core typically holds thousands of threads, where hardware threads are organized into warps. With the single instruction multiple thread (SIMT) pipeline, GPGPU can achieve high performance. But threads taking different branches in the same warp violate SIMD style and cause branch divergence. To support this, a hardware stack is used to sequentially execute all branches. Hence branch divergence leads to performance degradation. This article represents the PDOM (post dominator) stack as a binary tree, and each leaf corresponds to a branch target. We propose a new PDOM stack called PDOM-ASI, which can schedule all the tree leaves. The new stack can hide more long operation latencies with more schedulable warps without the problem of warp over-subdivision. Besides, a multi-level warp scheduling policy is proposed, which lets part of the warps run ahead and creates more opportunities to hide the latencies. The simulation results show that our policies achieve 10.5% performance improvements over baseline policies with only 1.33% hardware area overhead.     

Improvement in deuterium recovery from water–isotope mixture by thermal diffusion in the device of branch columns

Deuterium recovery from water–isotopes mixture using thermal diffusion can be improved by employing the branch column device, instead of single column devices, with the same total column length. The remixing effect due to convection currents in a thermal diffusion column for heavy water enrichment is thus reduced and separation improvement increases when the flow rate or the total column length increases. The improvement in separation can reach about 50% for the numerical example given.     

A branch and bound enhanced genetic algorithm for scheduling a flowline manufacturing cell with sequence dependent family setup times

This paper addresses the permutation flowline manufacturing cell with sequence dependent family setup times problem with the objective to minimize the makespan criterion. We develop a cooperative approach including a genetic algorithm and a branch and bound procedure. The latter is probabilistically integrated in the genetic algorithm in order to enhance the current solution. Moreover, the application of the branch and bound algorithm is based upon the decomposition of the problem into subproblems. The performance of the proposed method is tested by numerical experiments on a large number of representative problems.