Optimal and Near-optimal Global Register Allocation Using 0–1 Integer Programming

This paper presents a fundamentally new approach to global register allocation that optimally allocates registers and optimally places spill code, significantly decreasing spill code overhead compared with the traditional graph-coloring approach. The Optimal Register Allocation (ORA) approach formulates global register allocation as a 0–1 integer programming problem, incorporating all aspects of register allocation within a unified framework, including copy elimination, live range splitting, rematerialization, callee and caller register spilling, special instruction-operand requirements, and paired registers. A prototype ORA allocator is built into the Gnu C Compiler (GCC). For the SPEC92 integer benchmarks, the ORA allocator actually produces a net decrease of more than 100 million cycles across the entire benchmark set, because the dynamic copies the ORA allocator removes exceed the dynamic loads and stores that are inserted. In contrast, the GCC allocator and a Chaitin-style graph-coloring allocator each cause a net increase of more than 1 billion cycles. Because global register allocation is NP-complete, optimal register allocation has been considered intractable. However, the run-time complexity of the ORA approach is shown experimentally to be O(n³). A profile-guided hybrid allocation approach is proposed that uses the ORA allocator for the performance critical regions in the performance critical functions, while using a graph-coloring allocator for the non-critical functions and regions. An ORA-GCC hybrid allocator takes an average of 4.6 seconds per function to produce an allocation that is within 1% of optimal for 97% of the SPEC92 integer benchmark functions, showing that the hybrid allocator is practical as an advanced optimization for performance-critical codes.     

求解0-1整数规划的混合粒子群优化算法

经典的粒子群是一个有效的寻找连续函数极值的方法,结合遗传算法的思想提出的混合粒子群算法来解决0-1整数规划问题,经过比较测试,6种混合粒子群算法的效果都比较好,特别交叉策略A和变异策略C的混合粒子群算法是最好的且简单有效的算法.对于目前还没有好的解法的组合优化问题,很容易地修改此算法就可解决.     

一类非线性整数规划问题的计算机求解

文章针对一类具有特殊性质的非线性整数规划问题,提出了一种具有剔除选择的计算机求解的方法。该求解方法的提出,对解决现实生活中这一类非线性整数规划问题,提供了一种切实可行的途径,这为此类问题的决策提供了有效的手段。     

An Integer Linear Programming Problem with Multi-Criteria and Multi-Constraint Levels: a Branch-and-Partition Algorithm

In this paper, we propose a branch-and-partition algorithm to solve the integer linear programming problem with multi-criteria and multi-constraint levels (MC-ILP). The procedure begins with the relaxation problem that is formed by ignoring the integer restrictions. In this branch-and-partition procedure, an MC linear programming problem is adopted by adding a restriction according to a basic decision variable that is not integer. Then the MC-simplex method is applied to locate the set of all potential solutions over possible changes of the objective coefficient parameter and the constraint parameter for a regular MC linear programming problem. We use parameter partition to divide the (λ, γ) space for integer solutions of MC problem. The branch-and-partition procedure terminates when every potential basis for the relaxation problem is a potential basis for the MC-ILP problem. A numerical example is used to demonstrate the proposed algorithm in solving the MC-ILP problems. The comparison study and discussion on the applicability of the proposed method are also provided.     

业务流程管理中的大规模整数规划问题求解

对从企业业务流程管理中抽象出来的大规模整数规划问题的计算机求解方法进行讨论。提出一种内存优化管理方法,能更高效地存储海量数据。同时对求解整数规划问题的经典算法——分枝定界算法进行研究,利用人工智能的搜索思想,给出分枝定界法的改进算法,使其能快速求解大规模整数规划问题。     

基于0-1整数规划的集装箱港口运输环境应用探究

近年来,随着集装箱港口的规模变化、科技的飞速发展,减少成本、提高效率成为集装箱港口的主要发展方向。多机器人系统具有低成本的优势,与路径规划算法相结合,使机器人替代人力进行作业,可以在很大程度上减少港口运营成本。笔者选取2000—2017年的各港口集装箱吞吐量总量、码头泊位数总数以及码头长度总数变化,分析集装箱港口硬件设施发展情况,结合相关影响因素与机器人路径规划算法,提出机器人在运输过程中需要考虑的问题和解决方案,说明机器人可以在港口环境下替代人力进行作业,从而减少港口运营成本,提高工作效率,为集装箱港口面向自动化和智能化提供理论参考。     

基于0-1型整数规划的任务打包方案研究

"拍照赚钱"是移动互联网下的一种自助式服务模式。用户下载APP,注册成为APP的会员,然后从APP上领取需要拍照的任务(比如上超市去检查某种商品的上架情况),赚取APP对任务所标定的酬金。这是一种基于移动互联网的自助式劳务众包平台,APP即为该平台运行的核心,而实际情况下,多个任务可能因为位置比较集中,导致用户会争相选择,一种考虑是将这些任务联合在一起打包发布,在这种考虑下,就需要修改原本的定价模型来匹配这种打包方案。针对这个问题,我们使用0-1规划、聚类分析等数学方法,运用了Lingo和XGeocoding软件,并结合实际情况,确定了定价模型。     

物流配送中心选址问题的0-1规划并行算法

建立物流配送中心选址问题的0-1混合整数规划模型,并结合目标排序法和改进的PSRS设计求解0-1规为1的并行算法。改进PSRS可将各个目标的验证任务进行均衡划分,并提交给各个处理器并行进行可行性验证,算法理论上具有接近处理器个数P的加速比。     

基于Visual C 的0-1背包问题的动态规划算法

0-1背包问题是经典的NP问题.本文对0-1背包问题的动态规划算法进行了分析,用Visual C 实现该算法.     

求解0-1规划问题的DNA计算模型(英文)

DNA计算是以DNA分子作为数据的一种新型计算模式．在DNA计算中首要面对的问题是编码问题．文中提出了一种双编码方法,利用这种编码方法可以使得在DNA计算的读解过程类似于DNA测序过程,容易实现自动化操作．基于该编码方法所建立的DNA计算模型可用于求解0-1规划问题,只需4次PCR反应即可读取问题的可行解．与其他DNA计算模型相比,该模型具有操作简单、易于实现的优点．     

简单0-1规划问题的动态DNA折纸计算模型

DNA折纸是一种全新的DNA自组装方法。将一个由DNA折纸卡槽、双态DNA机器、DNA行走机器人组装而成的动态折纸应用于求解0-1规划问题。其中DNA折纸卡槽由1条M13脚手架链和202条钉书钉链折叠而成。双态DNA机器分为不修饰和修饰金纳米颗粒两种情况,对应于0-1规划问题约束变量的取值为0或者1。DNA折纸卡槽和DNA双态机器组装成折纸基底。DNA行走机器人是7条单链折叠成的带有粘性末端的DNA折纸。在链的驱动下,DNA行走机器人在折纸基底上顺时针旋转行走,每步旋转120°。DNA行走机器人每走两步,与折纸基底上的DNA双态机器进行链置换,接收修饰的金纳米颗粒。当整个动态行走过程结束,根据透射电镜下DNA行走机器人接收的金纳米颗粒的大小和个数来判断约束变量的取值是否为可行解。该计算模型采用模块化结构,DNA折纸卡槽、双态DNA机器、DNA行走机器人等折纸均单独设计,且采用透射电镜读解,因而提高了模型实现的可行性。     

求解0-1规划的生长竞争蚁群算法

0-1规划是决策变量仅取值0或1的一类特殊的整数规划,具有深刻的背景和广泛的应用。植物的生长取决于对光资源的获取,本文将植物生长的竞争机制引入蚁群算法,给出了一种求解0-1规划的生长竞争蚁群优化算法。算法定义了0-1规划的生长竞争演化规则,建立了算法模型,提高了蚁群的全局优化能力。通过对多个实例的求解和验证,结果表明该方法是一种有效的方法。     

4PL路径优化问题0-1规划模型与求解

研究带有时间窗的第四方物流(fourth-party logistics,4PL)路径优化问题,在满足客户对配送时间要求的同时实现物流运输成本最小,以提供最优的配送方案.根据问题本身的特点,建立了带有时间窗的4PL路径优化问题的0-1整数规划模型,采用CPLEX软件分别求解了7节点、15节点和30节点的算例.将算例结果与基于路进行建模的和声搜索算法和枚举算法进行了对比,结果表明CPLEX可以为带有时间窗的4PL路径优化问题提供最优的解决方案,验证了模型的有效性.     

Relay feedback auto-tuning of process controllers — a tutorial review

The PID relay auto-tuner of Astrom–Hagglund is one of the simplest and most robust auto-tuning techniques for process controllers and has been successfully applied to industry for more than 15 years. This tuner is based on an approximate estimation of the critical point on the process frequency response from relay oscillations. Many developments have recently been reported to extend its applications. It turns out that more and accurate information on process dynamics can be obtained from the same relay test with the help of new identification techniques, and used to tune PID controllers better. Extensions are also made to tune model-based advanced controllers and multivariable controllers. The present paper reviews these developments and shows the state-of art in relay auto-tuning of process controllers.     

求解0-1二次规划问题的迭代禁忌搜索算法

提出迭代禁忌算法求解0-1二次规划问题。在局部搜索过程中,使用禁忌搜索贪心跳坑策略,能够使算法有效跳出局部最优值的陷阱。采用国际上公认的30个算例作为算法测试实验集,与传统的禁忌搜索、模拟退火算法以及混合算法进行比较。实验结果表明,该算法在所有算例上都能够得到文献中报告的最优解,且计算效率明显优于其他算法。     

The Language Evolver Lever — Tool Demonstration —

Since many domains are constantly evolving, the associated domain specific languages (DSL) inevitably have to evolve too, to retain their value. But the evolution of a DSL can be very expensive, since existing words of the language (i.e. programs) and tools have to be adapted according to the changes of the DSL itself. In such cases, these costs seriously limit the adoption of DSLs.This paper presents Lever, a tool for the evolutionary development of DSLs. Lever aims at making evolutionary changes to a DSL much cheaper by automating the adaptation of the DSL parser as well as existing words and providing additional support for the correct adaptation of existing tools (e.g. program generators). This way, Lever simplifies DSL maintenance and paves the ground for bottom-up DSL development.     

整数规划的新算法

整数规划是NP困难的经典问题之一，将传统的二分搜索方法推广应用到整数规划的解空间中，提出一种求解整数规划的新算法。当问题变量数固定时，算法的时间复杂性为0(Llog^L)，其中L为问题实例的输入规模，理论分析和实验结果表明：新算法不仅初步解决了目前求解系数呈指数增长的整数规划问题时存在的实质性困难，可直接用于此类大规模问题的求解，同时由于其特剐适合并行处理的算法结构，可望为一般大规模整数规划问题的精确求解提供新的途径。     

Two-Stage Stochastic Integer Programming Model for Multiperiod Sea Cargo Mix Problem in Container Shipping Industry

In this paper, we consider the cargo mix problem under uncertainty in the container shipping industry. We seek to determine the optimal cargo mix in a multiperiod planning horizon with the objective of maximizing the total expected profit derived from all freight bookings received in the planning horizon. We present a two-stage stochastic integer programming model and propose a heuristic algorithm for multiperiod sea cargo mix problem under uncertainty. Finally, numerical experiments on a wide range of randomly generated problem instances are conducted to demonstrate the efficiency of the algorithm.