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| -- 作者:sciadmin -- 发布时间:2005/3/12 22:34:27 -- Lagrange-type Functions in Constrained Non-Convex Optimization Lectures on
Lagrange-type Functions in Constrained Non-Convex Optimization
by Professor Yang Xiaoqi Department of Applied Mathematics The
Background
Lagrange and penalty function methods provide a powerful approach, both as a theoretical tool and a computational vehicle, for the study of constrained optimization problems. However, for a nonconvex constrained optimization problem, the classical Lagrange primal-dual method may fail to find a minimum as a zero duality gap is not always guaranteed. A large penalty parameter is, in general, required for classical quadratic penalty functions in order that minima of penalty problems are a good approximation to those of the original constrained optimization problems. It is well-known that penalty functions with too large parameters cause an obstacle for numerical implementation. Thus the question arises how to generalize classical Lagrange and penalty functions, in order to obtain an appropriate scheme for reducing constrained optimization problems to unconstrained ones that will be suitable for sufficiently broad classes of optimization problems from both the theoretical and computational viewpoints.
In these lectures, we will investigate the following topics:
1) Duality and exact penalization via a generalized augmented Lagrangian. 2) Partially strictly monotone and nonlinear penalty functions for constrained mathematical programs.
About the Speaker
Dr Yang Xiaoqi is an Associate Professor in Department of Applied Mathematics, The Hong Kong Polytechnic University. He got his BSc degree in
* * * ALL ARE WELCOME * * * [此贴子已经被作者于2005-3-14 10:40:11编辑过]
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| -- 作者:mathman -- 发布时间:2005/3/14 16:50:25 -- 上述学术讲座的时间与地点: 时 间: (1)2005年3月24日(星期四)下午3:00 (2)2005年3月25日(星期五)下午3:00 地 点:理科楼207 欢迎广大师生参加! |
| -- 作者:optccr -- 发布时间:2005/3/14 23:55:29 -- 太好了! |