灰色非线性约束规划的改进引力搜索算法求解

灰色非线性约束规划是灰色系统中一个重要的优化问题.为求解灰色非线性约束规划,给出了一种改进引力搜索算法的求解方法.实验结果表明改进引力搜索算法对求解灰色非线性约束规划可行有效.     

求解随机凸规划概率约束问题的对偶算法

1引言随机规划中的概率约束问题在工程和管理中有广泛的应用.因为问题中包含非线性的概率约束,它们的求解非常困难.如果目标函数是线性的,问题的求解就比较容易.给出了一个求解随机线性规划概率约束问题的综述.原-对偶算法和切平面算法是比较有效的.在本文中,我们讨论随机凸规划概率约束问题:     

ATCase:一个基于多项式约束求解的数值程序测试用例自动生成工具北大核心CSCD

现有的基于符号执行的测试用例自动生成技术存在不足之处:由于精度限制和非线性约束求解的复杂性,符号执行在遇到复杂的非线性浮点约束时效果并不理想.针对这一现状,给出了一个基于多项式约束求解和区间验证的测试用例生成算法.对于复杂非线性约束难以求解的问题,采用基于低秩矩量矩阵恢复的多项式系统求解方法,该方法对于含有等式和不等式的多项式系统,相较于其他方法求解速度更快,更适合大规模问题的求解;对于浮点约束求解不准确的问题,采用基于区间分析的验证算法来计算包含精确实解的区间,基于该区间给出测试用例,可以避免浮点计算的不准确和异常.结合该算法和符号执行工具KLEE-FP实现了一个测试用例自动生成工具ATCase(automatically generate test case),它能够分析数值程序中的路径并自动生成满足路径约束的测试用例.在两个开源软件库中的2两个复杂的真实程序上运行的实验结果表明ATCase相比KLEE-FP所使用的STP求解器,能快速生成具有更高覆盖率的测试用例,特别是在处理相对复杂的非线性约束时,优势更加明显.     

ATCase:一个基于多项式约束求解的数值程序测试用例自动生成工具北大核心CSCD

现有的基于符号执行的测试用例自动生成技术存在不足之处:由于精度限制和非线性约束求解的复杂性,符号执行在遇到复杂的非线性浮点约束时效果并不理想.针对这一现状,给出了一个基于多项式约束求解和区间验证的测试用例生成算法.对于复杂非线性约束难以求解的问题,采用基于低秩矩量矩阵恢复的多项式系统求解方法,该方法对于含有等式和不等式的多项式系统,相较于其他方法求解速度更快,更适合大规模问题的求解;对于浮点约束求解不准确的问题,采用基于区间分析的验证算法来计算包含精确实解的区间,基于该区间给出测试用例,可以避免浮点计算的不准确和异常.结合该算法和符号执行工具KLEE-FP实现了一个测试用例自动生成工具ATCase(automatically generate test case),它能够分析数值程序中的路径并自动生成满足路径约束的测试用例.在两个开源软件库中的2两个复杂的真实程序上运行的实验结果表明ATCase相比KLEE-FP所使用的STP求解器,能快速生成具有更高覆盖率的测试用例,特别是在处理相对复杂的非线性约束时,优势更加明显.     

ATCase:一个基于多项式约束求解的数值程序测试用例自动生成工具北大核心CSCD

现有的基于符号执行的测试用例自动生成技术存在不足之处:由于精度限制和非线性约束求解的复杂性,符号执行在遇到复杂的非线性浮点约束时效果并不理想.针对这一现状,给出了一个基于多项式约束求解和区间验证的测试用例生成算法.对于复杂非线性约束难以求解的问题,采用基于低秩矩量矩阵恢复的多项式系统求解方法,该方法对于含有等式和不等式的多项式系统,相较于其他方法求解速度更快,更适合大规模问题的求解;对于浮点约束求解不准确的问题,采用基于区间分析的验证算法来计算包含精确实解的区间,基于该区间给出测试用例,可以避免浮点计算的不准确和异常.结合该算法和符号执行工具KLEE-FP实现了一个测试用例自动生成工具ATCase(automatically generate test case),它能够分析数值程序中的路径并自动生成满足路径约束的测试用例.在两个开源软件库中的2两个复杂的真实程序上运行的实验结果表明ATCase相比KLEE-FP所使用的STP求解器,能快速生成具有更高覆盖率的测试用例,特别是在处理相对复杂的非线性约束时,优势更加明显.     

分布鲁棒机会约束优化问题的研究

机会约束作为求解随机优化问题的重要方法之一,在金融、工程、管理等领域均有着广泛的应用.随着实际问题呈现越来越复杂的不确定性状态,随机变量分布的准确信息难以预测,分布鲁棒机会约束作为有效求解随机变量信息模糊(不完备)下的随机优化问题被提出.近几年,研究者们不断提出分布鲁棒机会约束新的模型理论和算法.现总结了求解不同类型分布鲁棒机会约束问题的建模、模型求解、算法及应用的新进展.     

ATCase:一个基于多项式约束求解的数值程序测试用例自动生成工具

现有的基于符号执行的测试用例自动生成技术存在不足之处:由于精度限制和非线性约束求解的复杂性,符号执行在遇到复杂的非线性浮点约束时效果并不理想.针对这一现状,给出了一个基于多项式约束求解和区间验证的测试用例生成算法.对于复杂非线性约束难以求解的问题,采用基于低秩矩量矩阵恢复的多项式系统求解方法,该方法对于含有等式和不等式的多项式系统,相较于其他方法求解速度更快,更适合大规模问题的求解;对于浮点约束求解不准确的问题,采用基于区间分析的验证算法来计算包含精确实解的区间,基于该区间给出测试用例,可以避免浮点计算的不准确和异常.结合该算法和符号执行工具KLEE-FP实现了一个测试用例自动生成工具ATCase(automatically generate test case),它能够分析数值程序中的路径并自动生成满足路径约束的测试用例.在两个开源软件库中的2两个复杂的真实程序上运行的实验结果表明ATCase相比KLEE-FP所使用的STP求解器,能快速生成具有更高覆盖率的测试用例,特别是在处理相对复杂的非线性约束时,优势更加明显.     

关于非线性等式约束优化问题的一点注记

给出了一种求解非线性约束优化问题的算法.利用Lagrange函数,将非线性约束优化问题转化为无约束优化问题,从而得到解决.方法仅仅依靠求解一个线性方程组来求解,因此使得计算量减小,计算速度变快.在一定条件下,给出算法的收敛性证明.数值试验表明方法是有效的.     

低阶精确罚函数的一种二阶光滑逼近

给出了求解约束优化问题的低阶精确罚函数的一种二阶光滑逼近方法,证明了光滑后的罚优化问题的最优解是原约束优化问题的ε-近似最优解,基于光滑后的罚优化问题,提出了求解约束优化问题的一种新的算法,并证明了该算法的收敛性,数值例子表明该算法对于求解约束优化问题是有效的.     

求解一类逆鲁棒优化问题的牛顿型扰动方法

逆优化问题是指通过调整目标函数和约束中的某些参数使得已知的一个解成为参数调整后的优化问题的最优解.本文考虑求解一类逆鲁棒优化问题.首先,我们将该问题转化为带有一个线性等式约束,一个二阶锥互补约束和一个线性互补约束的极小化问题;其次,通过一类扰动方法来对转化后的极小化问题进行求解,然后利用带Armijo线搜索的非精确牛顿法求解每一个扰动问题.最后,通过数值实验验证该方法行之有效.     

二次自伴矩阵多项式阵特征值界的数值计算

在参数不确定性线性系统的鲁棒控制研究中,常用到的一个指标就是使不确定性系统在输出反馈或状态反馈控制下的闭环系统在H∞-范数界γ的条件下的二次稳定.是否二次稳定,一般要验证能否找到一个正常数,ε使相应的R iccati方程有正定解.而R iccati方程一般情况下求解相当困难.本文通过具体的分析,提出了一种在给定正定矩阵的条件下,找使此正定阵是R iccati方程的解相对应的正常数ε的可能范围的方法,即求解二次自伴矩阵多项式阵特征值界的方法.文中详细给出了所用理论及算法.给出了求正常数ε范围的一个实例.     

Modified Alternating Direction Methods for the Modified Multiple-Sets Split Feasibility Problems

In this paper, we propose two new multiple-sets split feasibility problem models and new solution methods. The first model is more separable than the original one, which enables us to apply a modified alternating direction method with parallel steps to solve it. Then, to overcome the difficulty of computing projections onto the constraint sets, a special version of this modified method with the strategy of projection onto half-space is given. The second model consists in finding a least Euclidean norm solution of the multiple-sets split feasibility problem, for which we provide another modified alternating direction method. Numerical results presented at the last show the efficiency of our methods.     

超AKNS方程新的可积分解

该文介绍从3×3矩阵形式超谱问题出发, 构造新高阶矩阵形式超谱问题的方法.以超AKNS方程为例, 作者构造了5×5矩阵形式的超AKNS谱问题并且运用双非线性化方法,给出了超AKNS方程的新约束, 得到该约束下超AKNS方程新的可积分解.     

A new upper bound for the 0-1 quadratic knapsack problem

The 0-1 quadratic knapsack problem (QKP) consists in maximizing a positive quadratic pseudo-Boolean function subject to a linear capacity constraint. We present in this paper a new method, based on Lagrangian decomposition, for computing an upper bound of QKP. We report computational experiments which demonstrate the sharpness of the bound (relative error very often less than 1%) for large size instances (up to 500 variables).     

Linear programming for the 0–1 quadratic knapsack problem

In this paper we consider the quadratic knapsack problem which consists in maximizing a positive quadratic pseudo-Boolean function subject to a linear capacity constraint. We propose a new method for computing an upper bound. This method is based on the solution of a continuous linear program constructed by adding to a classical linearization of the problem some constraints rebundant in 0–1 variables but nonredundant in continuous variables. The obtained upper bound is better than the bounds given by other known methods. We also propose an algorithm for computing a good feasible solution. This algorithm is an elaboration of the heuristic methods proposed by Chaillou, Hansen and Mahieu and by Gallo, Hammer and Simeone. The relative error between this feasible solution and the optimum solution is generally less than 1%. We show how these upper and lower bounds can be efficiently used to determine the values of some variables at the optimum. Finally we propose a branch-and-bound algorithm for solving the quadratic knapsack problem and report extensive computational tests.     

An efficient method for analyzing the solutions of the Korteweg-de Vries equation

A new iterative method is applied to study the solutions of the Korteweg-de Vries (KdV) equation. The method is a modified form of the well known Adomian decomposition method (ADM), where it avoids the difficulty of computing the Adomian polynomials. We prove the existence of a unique solution of the KdV equation. And then, we show that the new method generates an infinite series which converges uniformly to the exact solution of the problem. Soliton solutions of the KdV equation are obtained by the new method. Numerical calculations indicate the effectiveness of the new method where the obtained results are very accurate and better than the ones obtained by the ADM.     

A CSP Versus a Zonotope-Based Method for Solving Guard Set Intersection in Nonlinear Hybrid Reachability

Computing the reachable set of hybrid dynamical systems in a reliable and verified way is an important step when addressing verification or synthesis tasks. This issue is still challenging for uncertain nonlinear hybrid dynamical systems. We show in this paper how to combine a method for computing continuous transitions via interval Taylor methods and a method for computing the geometrical intersection of a flowpipe with guard sets, to build an interval method for reachability computation that can be used with truly nonlinear hybrid systems. Our method for flowpipe guard set intersection has two variants. The first one relies on interval constraint propagation for solving a constraint satisfaction problem and applies in the general case. The second one computes the intersection of a zonotope and a hyperplane and applies only when the guard sets are linear. The performance of our method is illustrated on examples involving typical hybrid systems.     

Null Controllability with Constraints on the State for the Semilinear Heat Equation

We consider a null controllability problem for the semilinear heat equation with finite number of constraints on the state. Interpreting each constraint by means of adjoint state notion, we transform the linearized problem into an equivalent linear problem of null controllability with constraint on the control. Using inequalities of observability adapted to the constraint, we solve the equivalent problem. Then, by a fixed-point method, we prove the main result.     

Optimal investment-reinsurance policy for an insurance company with VaR constraint

This paper investigates an investment-reinsurance problem for an insurance company that has a possibility to choose among different business activities, including reinsurance/new business and security investment. Our main objective is to find the optimal policy to minimize its probability of ruin. The main novelty of this paper is the introduction of a dynamic Value-at-Risk (VaR) constraint. This provides a way to control risk and to fulfill the requirement of regulators on market risk. This problem is formulated as an infinite horizontal stochastic control problem with a constrained control space. The dynamic programming technique is applied to derive the Hamilton-Jacobi-Bellman (HJB) equation and the Lagrange multiplier method is used to tackle the dynamic VaR constraint. Closed-form expressions for the minimal ruin probability as well as the optimal investment-reinsurance/new business policy are derived. It turns out that the risk exposure of the insurance company subject to the dynamic VaR constraint is always lower than otherwise. Finally, a numerical example is given to illustrate our results.     

The conversion of Fredholm integral equations to equivalent Cauchy problems— II. computation of resolvents

A new method is given for computing the resolvent of a large class of Fredholm integral equations. The technique is based on converting the integral equation satisfied by the resolvent to a family of two point boundary value problems. The application of invariant imbedding then gives an equivalent Cauchy problem satisfied by the resolvent kernel. The procedure is compared to previous ones based on the Bellman—Krein equation. It is shown that our method requires fewer equations to integrate if the number of output points on each axis exeeds the bank of the kernel.