Adaptive nonmonotone line search method for unconstrained optimization

In this paper, an adaptive nonmonotone line search method for unconstrained minimization problems is proposed. At every iteration, the new algorithm selects only one of the two directions: a Newton-type direction and a negative curvature direction, to perform the line search. The nonmonotone technique is included in the backtracking line search when the Newton-type direction is the search direction. Furthermore, if the negative curvature direction is the search direction, we increase the steplength under certain conditions. The global convergence to a stationary point with second-order optimality conditions is established. Some numerical results which show the efficiency of the new algorithm are reported.      

Two local search approaches for solving real-life car sequencing problems

The NP-hard problem of car sequencing appears as the heart of the logistic process of many car manufacturers. The subject of the ROADEF’2005 challenge addressed a car sequencing problem proposed by the car manufacturer RENAULT, more complex than the academic problem generally addressed in the literature. This paper describes two local search approaches for this problem. In the first part, a new approach by very large-scale neighborhood search is presented. This approach, designed during the qualification stage preceding the final, is based on an original integer linear programming formulation. The second part is dedicated to the approach which enabled us to win the ROADEF’2005 challenge. Inspired by the latest works on the subject, this one is based on very fast explorations of small neighborhoods. Our contribution here is mainly algorithmic, in particular by showing how much exploiting invariants speeds up the neighborhood evaluation and contributes to the diversification of the search. Finally, the two approaches are compared and discussed through an extensive computational study on RENAULT’s benchmarks. The main conclusion drawn at this point is that sophisticated metaheuristics are useless to solve car sequencing problems. More generally, our victory on ROADEF’2005 challenge demonstrates that algorithmic aspects, sometimes neglected, remain the key ingredients for designing and engineering high-performance local search heuristics.     

On the Dirac equation in the algebraic approximation

It is shown that the restrictive conditions of Wood et al. [1] are not necessary to reach the conclusion that the Dirac hamiltonian, projected onto the space of the large component, exhibits variational properties. The eigenvalue spectrum of matrix approximations to the partitioned hamiltonian (obtained by matrix partitioning) converges to the exact spectrum in the limit of infinite order (assuming completeness) but not necessarily from above as for true matrix representations (obtained from operator partitioning). Optimization of non-linear parameters is shown not to cause variational instabilities.Control Data Corporation PACER fellow 1984–1986     

A new multi-objective genetic algorithm applied to hot-rolling process

A new genetic algorithms based multi-objective optimization algorithm (NMGA) has been developed during study. It works on a neighborhood concept in the functional space, utilizes the ideas on weak dominance and ranking and uses its own procedures for population sizing. The algorithm was successfully tested with some standard test functions, and when applied to a real-life data of the hot-rolling campaign of an integrated steel plant, it outperformed another recently developed multi-objective evolutionary algorithm.     

Reduced Vertex Set Result for Interval Semidefinite Optimization Problems

In this paper we propose a reduced vertex result for the robust solution of uncertain semidefinite optimization problems subject to interval uncertainty. If the number of decision variables is m and the size of the coefficient matrices in the linear matrix inequality constraints is n×n, a direct vertex approach would require satisfaction of 2 ^{n(m+1)(n+1)/2} vertex constraints: a huge number, even for small values of n and m. The conditions derived here are instead based on the introduction of m slack variables and a subset of vertex coefficient matrices of cardinality 2 ⁿ⁻¹, thus reducing the problem to a practically manageable size, at least for small n. A similar size reduction is also obtained for a class of problems with affinely dependent interval uncertainty. This work is supported by MIUR under the FIRB project “Learning, Randomization and Guaranteed Predictive Inference for Complex Uncertain Systems,” and by CNR RSTL funds.     

压缩空气蓄能(CAES)系统集成及性能计算

压缩空气蓄能(CAES)系统是一种新型电能存储系统,具有动态响应快、经济性能高、环境污染小等优点,可起到负荷平衡、战略规划、提高供电质量的作用。CAES系统包括空气压缩机、电动机/发电机、地下贮气室、换热器、燃烧室、燃气轮机等常用设备,可分为两个子系统,分别是蓄能子系统和发电子系统,通过对设备采取不同组态,可得到不同方案系统。本文将结合当前CAES发展的新技术,提出CAES系统的集成方案并进行相应的计算与优化。     

波动方程反演问题的一种新的逼近方法

本文给出了一种求解波动方程反演问题的“多目标函数法”.这种方法简单、有效,并具有明确的物理意义.对于三维问题的程序化它有很强的优越性.     

A survey on the continuous nonlinear resource allocation problem

Our problem of interest consists of minimizing a separable, convex and differentiable function over a convex set, defined by bounds on the variables and an explicit constraint described by a separable convex function. Applications are abundant, and vary from equilibrium problems in the engineering and economic sciences, through resource allocation and balancing problems in manufacturing, statistics, military operations research and production and financial economics, to subproblems in algorithms for a variety of more complex optimization models. This paper surveys the history and applications of the problem, as well as algorithmic approaches to its solution. The most common techniques are based on finding the optimal value of the Lagrange multiplier for the explicit constraint, most often through the use of a type of line search procedure. We analyze the most relevant references, especially regarding their originality and numerical findings, summarizing with remarks on possible extensions and future research.     

Banach空间的张量积及其共轭空间

张量积函子是同调代数中研究模范畴的重要工具。在[1]的基础上,本文对B-空间的张量积做了讨论,得出一些新的结论。设E是B-空间,{E_i,j∈J}是B-空间族,作为赋范空间,则E(?)∪_(i∈J)E_i与∪_(i∈J)(E(?)E_i)等距同构。作为B-空间,E(?)_(?)∪_(i=1)~(?)E_i与∪_(i=1)~(?)(E(?)_(β_(?))E_i)的子空间等距同构。其次本文推广了著名的伴随同构定理([2]Th2.11).设E_1,E_2与F是B-空间,则(?)(E_1(?)_(?)E_2,F)分别与(?)(E_2,(?)(E_1,F)),(?)(E_1,(?)(E_2,F))等距同构.特别(E_1(?)_(?)E_2)分别与(?)(E_2,E_1),(?)(E_1,E_2)等距同构.最后,设E_i,F_i是B-空间,f∈(?)(E_1,F_1),g∈(?)(E_2,F_2),则存在唯一的φ∈(?)(E_1(?)_(β_1)E_2,F_1(?)_(β_2)F_2),记φ=f(?)g.令P={sum from i to f_i(?)g_i},则P与(?)(E_1,F_1)(?)_(?)(?)(E_2,F_2)的稠密子空间(?)(E_1,F_1)(?)(E_2,F_2)等距同构。特别E_1(?)E_2是(E_1(?)_(β_1)E_2)的子空间。本文中的记号同于[1]。文中涉及到张量积的范数都是Cross-范数。     

Crowding with nearest neighbors replacement for multiple species niching and building blocks preservation in binary multimodal functions optimization

This paper introduces a novel niching scheme called the q-nearest neighbors replacement (q-NNR) method in the framework of the steady-state GAs (SSGAs) for solving binary multimodal optimization problems. A detailed comparison of the main niching approaches are presented first. The niching paradigm and difference of the selection-recombination genetic algorithms (GAs) and the recombination-replacement SSGAs are discussed. Then the q-NNR is developed by adopting special replacement policies based on the SSGAs; a Boltzmann scheme for dynamically sizing the nearest neighbors set is designed to achieve a speed-up and control the proportion of individuals adapted to different niches. Finally, experiments are carried out on a set of test functions characterized by deception, epistasis, symmetry and multimodality. The results are satisfactory and illustrate the effectivity and efficiency of the proposed niching method.