具有算子与有界约束的无穷维规划问题的最优性条件

本文给出了当all-at-once方法用于求解最优控制问题而产生的一类同时具有算子和对部分变量具有简单界约束的无穷维最优化问题的一个一阶必要条件,构造了相应的信赖域子问题,据此,信赖域法可以用于求解无穷空间中的最优化问题。     

等式与界约束非线性优化信赖域算法的全局收敛

§ 1　ＩｎｔｒｏｄｕｃｔｉｏｎＣｏｎｓｉｄｅｒｔｈｅｆｏｌｌｏｗｉｎｇｎｏｎｌｉｎｅａｒｏｐｔｉｍｉｚａｔｉｏｎｐｒｏｂｌｅｍ :ｍｉｎｉｍｉｚｅｆ(ｘ)ｓｕｂｊｅｃｔｔｏＣ(ｘ) =0 ,　ａ≤ｘ≤ｂ ,( 1 .1 )ｗｈｅｒｅｆ(ｘ) :Ｒｎ→Ｒ ,Ｃ(ｘ) =(ｃ1(ｘ) ,ｃ2 (ｘ) ,...,ｃｍ(ｘ) ) Ｔ:Ｒｎ→Ｒｍ ａｒｅｔｗｉｃｅｃｏｎｔｉｎｕｏｕｓｌｙｄｉｆｆｅｒｅｎｔｉａｂｌｅ,ｍ≤ｎ ,ａ ,ｂ∈Ｒｎ.Ｔｒｕｓｔｒｅｇｉｏｎａｌｇｏｒｉｔｈｍｓａｒｅｖｅｒｙｅｆｆｅｃｔｉｖｅｆｏｒｓｏｌｖｉｎｇｎｏｎｌｉｎｅａｒｏｐｔｉｍｉ…     

Sensitivity of trust-region algorithms to their parameters

In this paper, we examine the sensitivity of trust-region algorithms on the parameters related to the step acceptance and update of the trust region. We show, in the context of unconstrained programming, that the numerical efficiency of these algorithms can easily be improved by choosing appropriate parameters. Recommended ranges of values for these parameters are exhibited on the basis of extensive numerical tests. MSC classification: 65K05, 90C26, 90C30     

A trust-region and affine scaling algorithm for linearly constrained optimization

A new trust-region and affine scaling algorithm for linearly constrained optimization is presented in this paper. Under no nondegenerate assumption, we prove that any limit point of the sequence generated by the new algorithm satisfies the first order necessary condition and there exists at least one limit point of the sequence which satisfies the second order necessary condition. Some preliminary numerical experiments are reported. The work was done while visiting Institute of Applied Mathematics, AMSS, CAS.     

局部误差的有界约束非线性方程组的非单调信赖域算法

本文提供了在没有非奇异假设的条件下,求解有界约束半光滑方程组的投影信赖域算法.基于一个正则化子问题,求得类牛顿步,进而求得投影牛顿步.在合理的假设条件下,证明了算法不仅具有整体收敛性而且保持超线性收敛速率.     

On the Global Convergence of a Projective Trust Region Algorithm for Nonlinear Equality Constrained Optimization

A trust-region sequential quadratic programming (SQP) method is developed and analyzed for the solution of smooth equality constrained optimization problems. The trust-region SQP algorithm is based on filter line search technique and a composite-step approach, which decomposes the overall step as sum of a vertical step and a horizontal step. The algorithm includes critical modifications of horizontal step computation. One orthogonal projective matrix of the Jacobian of constraint functions is employed in trust-region subproblems. The orthogonal projection gives the null space of the transposition of the Jacobian of the constraint function. Theoretical analysis shows that the new algorithm retains the global convergence to the first-order critical points under rather general conditions. The preliminary numerical results are reported.     

Active-Set Projected Trust-Region Algorithm for Box-Constrained Nonsmooth Equations

In this paper, by means of an active-set strategy, we present a trust-region method for solving box-constrained nonsmooth equations. Nice properties of the proposed method include: (a) all iterates remain feasible; (b) the search direction, as adequate combination of the projected gradient direction and the trust-region direction, is an asymptotic Newton direction under mild conditions; (c) the subproblem of the proposed method, possessing the form of an unconstrained trust-region subproblem, can be solved by existing methods; (d) the subproblem of the proposed method is of reduced dimension, which is potentially cheaper when applied to solve large-scale problems. Under appropriate conditions, we establish global and local superlinear/quadratic convergence of the method. Preliminary numerical results are given.     

Global convergence without the assumption of linear independence for a trust-region algorithm for constrained optimization

A trust-region algorithm for solving the equality constrained optimization problem is presented. This algorithm uses the Byrd and Omojokun way of computing the trial steps, but it differs from the Byrd and Omojokun algorithm in the way steps are evaluated. A global convergence theory for this new algorithm is presented. The main feature of this theory is that the linear independence assumption on the gradients of the constraints is not assumed.This research was supported in part by the Center for Research on Parallel Computation, by Grant NSF-CCR-91-20008, and by the REDI Foundation.     

Scenario analysis via bundle decomposition

Scenario analysis, originally proposed by Rockafellar and Wets, is a widely applicable method for introducing uncertainty into practical decision problems. As it often leads to very large optimization problems, one needs special techniques for the resulting numerical computation. One such technique, the Progressive Hedging Algorithm, is simple and universally applicable, but it can be slow. In this paper we show how the bundle decomposition method can be applied to linear or convex scenario analysis problems that are loosely coupled. We illustrate its effectiveness by presenting computational results for military force planning problems and for multi-scenario network models of production planning.The research reported here was sponsored by the National Science Foundation under Grant CCR-9109345, by the Air Force Systems Command, USAF, under Grants AFOSR-91-0089 and F49620-93-1-0068, by the US Army Research Office under Contract DAAL03-89-K-0149 and Grant No. DAAL03-92-G-0408, and by the US Army Space and Strategic Defense Command under Contract No. DASG60-91-C-0144. The US Government has certain rights in this material, and is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon.