基于DC分解的非凸二次规划SDP近似解

本文提出一类基于DC分解的非凸二次规划问题SDP松弛方法,并通过求解一个二阶锥问题得到原问题的近似最优解.我们首先对非凸二次目标函数进行DC分解,然后利用线性下逼近得到一个凸二次松弛问题,而最优的DC分解可通过求解一个SDP问题得到.数值试验表明,基于DC分解的SDP近似解平均优于经典SDP松弛和随机化方法产生的近似解。     

基于半定规划松弛的凸二层规划问题算法研究

提出使用凸松弛的方法求解二层规划问题,通过对一般带有二次约束的二次规划问题的半定规划松弛的探讨,研究了使用半定规划(SDP)松弛结合传统的分枝定界法求解带有凸二次下层问题的二层二次规划问题,相比常用的线性松弛方法,半定规划松弛方法可快速缩小分枝节点的上下界间隙,从而比以往的分枝定界法能够更快地获得问题的全局最优解.     

一类比式和问题的全局优化方法

对于一类比式和问题(P)给出一全局优化算法.首先利用线性约束的特征推导出问题(P)的等价问题(P1),然后利用新的线性松弛方法建立了问题(P1)的松弛线性规划(RLP),通过对目标函数可行域线性松弛的连续细分以及求解一系列线性规划,提出的分枝定界算法收敛到问题(P)的全局最优解.最终数值实验结果表明了该算法的可行性和高效性.     

稳健交通均衡模型的SDP松弛

交通均衡问题在城市交通管理中具有重要意义.研究均衡交通的目的是通过对稳定交通流进行量化分析、,为决策者提供交通规划及管理的依据.Wardrop交通均衡原理是描述交通均衡问题的基石,本文在其扩展之一的稳健Wardrop(Robust Wardrop,简记为RW)互补均衡模型的基础上,将不确定因素的盒子约束改进为球约束,以改善原有模型的保守度.其次给出带有不确定因素的稳健Wardrop极小化形式及其确定性稳健对应模型(Robust Counterpart,简记为RC).最后通过SDP松弛手段将稳健对应模型(RC)松弛为容易的线性半定规划问题进行求解,并给出实例说明,为不确定因素影响下的交通均衡问题提供了一种新的有效模型及解法.     

求不定二次规划全局解的一个新算法

本文提出了一个求不定二次规划问题全局最优解的新算法.首先,给出了三种计算下界的方法:线性逼近法、凸松弛法和拉格朗日松弛法;并且证明了拉格朗日对偶界与通过凸松弛得到的下界是相等的;然后建立了基于拉格朗日对偶界和矩形两分法的分枝定界算法,并给出了初步的数值试验结果.     

求解非定常对流扩散方程的流函数松弛方法

提出了一种求解线性和非线性对流扩散方程的流函数松弛方法.方法的主要思想是利用流函数松弛近似将原始的方程转化成等价的松弛方程组,新的松弛方程组是带源项的双曲系统.通过稳定性分析可以知道新系统的耗散系数可由松弛系数调整.数值实现亦证明这个方法可以快速有效地描述对流扩散方程的解.     

一种新的二次约束二次规划问题的分支定界算法

本文为了获得二次约束二次规划(QCQP)问题的全局最优解,提出一种新的参数化线性松弛分支定界算法.该算法利用参数化线性松弛技术,得到(QCQP)的全局最小值的下界,并利用区域缩减技术以最大限度地删除不可行区域,加快该算法的收敛速度.数值实验表明,本文提出的算法是有效并且可行的.     

求解二次分配问题的拉格朗日松弛新方法

以改进的拉格朗日松弛(Lagrangian relaxation,LR)方法和二次分配问题(quadratic assignment problem,QAP)的线性化模型为基础,给出了求解QAP的拉格朗日松弛新方法,这为有效求解QAP提供了一种新的解决方案.通过求解二次分配基准问题库(QAPLIB)中的实际算例,从实验的角度说明了拉格朗日松弛新方法求解QAP的可行性及存在的不足之处,并对今后进一步的研究工作指明了方向.     

求线性多乘积规划问题的分支定界算法（英文）

为求解线性多乘积规划问题(LMP),本文提出一个新的全局优化算法.首先,利用二阶导数信息,给出了一个新的线性化松弛方法.其次,为了改进算法的收敛速度,提出一个区域删除技巧.最后,为求解LMP,设计了一个分支定界算法.理论上证明了算法的收敛性.数值实验结果显示本文方法是有效可行的.     

关于图划分问题的改进的近似算法

本文考虑NP-难的极大图划分(MAX-GP)问题.我们给出应用半定规划(SDP) 松弛的一个一般方法,并且给出包括极大方向割,稠密子图,极大顶点覆盖,极大割,和极大反割在内的图划分问题的改进的近似比.     

Convex relaxations for nonconvex quadratically constrained quadratic programming: matrix cone decomposition and polyhedral approximation

We present a decomposition-approximation method for generating convex relaxations for nonconvex quadratically constrained quadratic programming (QCQP). We first develop a general conic program relaxation for QCQP based on a matrix decomposition scheme and polyhedral (piecewise linear) underestimation. By employing suitable matrix cones, we then show that the convex conic relaxation can be reduced to a semidefinite programming (SDP) problem. In particular, we investigate polyhedral underestimations for several classes of matrix cones, including the cones of rank-1 and rank-2 matrices, the cone generated by the coefficient matrices, the cone of positive semidefinite matrices and the cones induced by rank-2 semidefinite inequalities. We demonstrate that in general the new SDP relaxations can generate lower bounds at least as tight as the best known SDP relaxations for QCQP. Moreover, we give examples for which tighter lower bounds can be generated by the new SDP relaxations. We also report comparison results of different convex relaxation schemes for nonconvex QCQP with convex quadratic/linear constraints, nonconvex quadratic constraints and 0–1 constraints.     

Exact Solutions of Some Nonconvex Quadratic Optimization Problems via SDP and SOCP Relaxations

We show that SDP (semidefinite programming) and SOCP (second order cone programming) relaxations provide exact optimal solutions for a class of nonconvex quadratic optimization problems. It is a generalization of the results by S. Zhang for a subclass of quadratic maximization problems that have nonnegative off-diagonal coefficient matrices of quadratic objective functions and diagonal coefficient matrices of quadratic constraint functions. A new SOCP relaxation is proposed for the class of nonconvex quadratic optimization problems by extracting valid quadratic inequalities for positive semidefinite cones. Its effectiveness to obtain optimal values is shown to be the same as the SDP relaxation theoretically. Numerical results are presented to demonstrate that the SOCP relaxation is much more efficient than the SDP relaxation.     

Lagrangian decomposition and mixed-integer quadratic programming reformulations for probabilistically constrained quadratic programs

Probabilistically constrained quadratic programming (PCQP) problems arise naturally from many real-world applications and have posed a great challenge in front of the optimization society for years due to the nonconvex and discrete nature of its feasible set. We consider in this paper a special case of PCQP where the random vector has a finite discrete distribution. We first derive second-order cone programming (SOCP) relaxation and semidefinite programming (SDP) relaxation for the problem via a new Lagrangian decomposition scheme. We then give a mixed integer quadratic programming (MIQP) reformulation of the PCQP and show that the continuous relaxation of the MIQP is exactly the SOCP relaxation. This new MIQP reformulation is more efficient than the standard MIQP reformulation in the sense that its continuous relaxation is tighter than or at least as tight as that of the standard MIQP. We report preliminary computational results to demonstrate the tightness of the new convex relaxations and the effectiveness of the new MIQP reformulation.     

Semidefinite Programming Relaxation for Nonconvex Quadratic Programs

This paper applies the SDP (semidefinite programming)relaxation originally developed for a 0-1 integer program to ageneral nonconvex QP (quadratic program) having a linear objective functionand quadratic inequality constraints, and presents some fundamental characterizations of the SDP relaxation including its equivalence to arelaxation using convex-quadratic valid inequalities for the feasible regionof the QP.     

Comparing SOS and SDP relaxations of sensor network localization

We investigate the relationships between various sum of squares (SOS) and semidefinite programming (SDP) relaxations for the sensor network localization problem. In particular, we show that Biswas and Ye’s SDP relaxation is equivalent to the degree one SOS relaxation of Kim et al. We also show that Nie’s sparse-SOS relaxation is stronger than the edge-based semidefinite programming (ESDP) relaxation, and that the trace test for accuracy, which is very useful for SDP and ESDP relaxations, can be extended to the sparse-SOS relaxation.     

Semidefinite relaxations for quadratically constrained quadratic programming: A review and comparisons

At the intersection of nonlinear and combinatorial optimization, quadratic programming has attracted significant interest over the past several decades. A variety of relaxations for quadratically constrained quadratic programming (QCQP) can be formulated as semidefinite programs (SDPs). The primary purpose of this paper is to present a systematic comparison of SDP relaxations for QCQP. Using theoretical analysis, it is shown that the recently developed doubly nonnegative relaxation is equivalent to the Shor relaxation, when the latter is enhanced with a partial first-order relaxation-linearization technique. These two relaxations are shown to theoretically dominate six other SDP relaxations. A computational comparison reveals that the two dominant relaxations require three orders of magnitude more computational time than the weaker relaxations, while providing relaxation gaps averaging 3% as opposed to gaps of up to 19% for weaker relaxations, on 700 randomly generated problems with up to 60 variables. An SDP relaxation derived from Lagrangian relaxation, after the addition of redundant nonlinear constraints to the primal, achieves gaps averaging 13% in a few CPU seconds.     

Faster,but weaker,relaxations for quadratically constrained quadratic programs

We introduce a new relaxation framework for nonconvex quadratically constrained quadratic programs (QCQPs). In contrast to existing relaxations based on semidefinite programming (SDP), our relaxations incorporate features of both SDP and second order cone programming (SOCP) and, as a result, solve more quickly than SDP. A downside is that the calculated bounds are weaker than those gotten by SDP. The framework allows one to choose a block-diagonal structure for the mixed SOCP-SDP, which in turn allows one to control the speed and bound quality. For a fixed block-diagonal structure, we also introduce a procedure to improve the bound quality without increasing computation time significantly. The effectiveness of our framework is illustrated on a large sample of QCQPs from various sources.     

An improved semidefinite programming relaxation for the satisfiability problem

The satisfiability (SAT) problem is a central problem in mathematical logic, computing theory, and artificial intelligence. An instance of SAT is specified by a set of boolean variables and a propositional formula in conjunctive normal form. Given such an instance, the SAT problem asks whether there is a truth assignment to the variables such that the formula is satisfied. It is well known that SAT is in general NP-complete, although several important special cases can be solved in polynomial time. Semidefinite programming (SDP) refers to the class of optimization problems where a linear function of a matrix variable X is maximized (or minimized) subject to linear constraints on the elements of X and the additional constraint that X be positive semidefinite. We are interested in the application of SDP to satisfiability problems, and in particular in how SDP can be used to detect unsatisfiability. In this paper we introduce a new SDP relaxation for the satisfiability problem. This SDP relaxation arises from the recently introduced paradigm of higher liftings for constructing semidefinite programming relaxations of discrete optimization problems. To derive the SDP relaxation, we first formulate SAT as an optimization problem involving matrices. Relaxing this formulation yields an SDP which significantly improves on the previous relaxations in the literature. The important characteristics of the SDP relaxation are its ability to prove that a given SAT formula is unsatisfiable independently of the lengths of the clauses in the formula, its potential to yield truth assignments satisfying the SAT instance if a feasible matrix of sufficiently low rank is computed, and the fact that it is more amenable to practical computation than previous SDPs arising from higher liftings. We present theoretical and computational results that support these claims.Mathematics Subject Classification (2000): 20E28, 20G40, 20C20     

Tightening a copositive relaxation for standard quadratic optimization problems

We focus in this paper the problem of improving the semidefinite programming (SDP) relaxations for the standard quadratic optimization problem (standard QP in short) that concerns with minimizing a quadratic form over a simplex. We first analyze the duality gap between the standard QP and one of its SDP relaxations known as “strengthened Shor’s relaxation”. To estimate the duality gap, we utilize the duality information of the SDP relaxation to construct a graph G ^?. The estimation can be then reduced to a two-phase problem of enumerating first all the minimal vertex covers of G ^? and solving next a family of second-order cone programming problems. When there is a nonzero duality gap, this duality gap estimation can lead to a strictly tighter lower bound than the strengthened Shor’s SDP bound. With the duality gap estimation improving scheme, we develop further a heuristic algorithm for obtaining a good approximate solution for standard QP.