A Lagrangian Formalism Based on the Velocity-Determined Virtual Displacements for Systems with Nonholonomic Constraints

We develop a Lagrangian formulation for classical systems with a general nonholonomic constraints by utilizing the so-called velocity-determined virtual-displacement conditions, i.e. by assuming the virtual displacements to be along the direction of the velocities in a special reference frame. It is shown that our general scheme encompasses as special cases the Chetaev and Voronets approaches when the constraints are homogeneous or linear in relative velocities.     

Surrogate constraint normalization for the set covering problem

The set covering problem (SCP) is central in a wide variety of practical applications for which finding good feasible solutions quickly (often in real-time) is crucial. Surrogate constraint normalization is a classical technique used to derive appropriate weights for surrogate constraint relaxations in mathematical programming. This framework remains the core of the most effective constructive heuristics for the solution of the SCP chiefly represented by the widely-used Chvátal method. This paper introduces a number of normalization rules and demonstrates their superiority to the classical Chvátal rule, especially when solving large scale and real-world instances. Directions for new advances on the creation of more elaborate normalization rules for surrogate heuristics are also provided.     

Optimality conditions in multiobjective differentiable programming

A theorem stated in Ref. 1 is corrected.     

SMALLEST g-SUPERSOLUTION FOR BSDE WITH CONTINUOUS DRIFT COEFFICIENTS

91. IntroductionSince the result of monotonic lied theorem of BSDE and its aPPlication to nonlinearDoolyMeyer decomposition theorem, the smallest g-supersolution with a constraint on (Y, Z)(see [1]), some developments have been done, for example, in 14], where by a penallzationmethod, Chen and Peng discussed nonlinear Doob-Meyer decomposition theorem ash theBSDE introduced by Duffie and EPsteinI3]. In [51 Lin discussed the smallest g-supersolutionto BSDE with a constraint on (y,z) wi…     

Integrating operations research in constraint programming

This paper presents constraint programming (CP) as a natural formalism for modelling problems, and as a flexible platform for solving them. CP has a range of techniques for handling constraints including several forms of propagation and tailored algorithms for global constraints. It also allows linear programming to be combined with propagation and novel and varied search techniques which can be easily expressed in CP. The paper describes how CP can be used to exploit linear programming within different kinds of hybrid algorithm. In particular it can enhance techniques such as Lagrangian relaxation, Benders decomposition and column generation.     

解约束优化问题的相容SQP滤子方法

提出了解约束优化问题的一类相容SQP滤子算法.利用序列二次规划方法结合信赖域技术计算试探步,而用滤子接受准则选择接受试探步.对二次规划子问题的不相容问题,应用Powell1978年于文[9]提出的方法对其约束引进参数进行了可行化处理.在一般条件下,算法具有全局收敛性.最后,数值试验显示了较好的结果.     

带模糊约束的最大流问题

首次提出带模糊约束的最大流问题,并根据网络中的弧容量限制是否带有模糊性,分别建立数学模型,给出求解这两个模型的相应算法和有关实例。