Gradient Projection Method for Optimization Problems with a Constraint in the Form of the Intersection of a Smooth Surface and a Convex Closed Set

Computational Mathematics and Mathematical Physics - The gradient projection method is generalized to the case of nonconvex sets of constraints representing the set-theoretic intersection of a...     

Convergence of the Gradient Projection Method for Generalized Convex Minimization

This paper develops convergence theory of the gradient projection method by Calamai and Moré (Math. Programming, vol. 39, 93–116, 1987) which, for minimizing a continuously differentiable optimization problem min{f(x) : x } where is a nonempty closed convex set, generates a sequence x_k+1 = P(x_k – _k f(x_k)) where the stepsize _k > 0 is chosen suitably. It is shown that, when f(x) is a pseudo-convex (quasi-convex) function, this method has strong convergence results: either x_k x^* and x^* is a minimizer (stationary point); or x_k arg min{f(x) : x } = , and f(x_k) inf{f(x) : x }.     

Global Convergence of a Modified Gradient Projection Method for Convex Constrained Problems

In this paper, the continuously differentiable optimization problem min{f（x） ： x∈Ω}, where Ω ∈ R^n is a nonempty closed convex set, the gradient projection method by Calamai and More （Math. Programming, Vol.39. P.93-116, 1987） is modified by memory gradient to improve the convergence rate of the gradient projection method is considered. The convergence of the new method is analyzed without assuming that the iteration sequence {x^k} of bounded. Moreover, it is shown that, when f（x） is pseudo-convex （quasiconvex） function, this new method has strong convergence results. The numerical results show that the method in this paper is more effective than the gradient projection method.     

求解约束优化问题的记忆梯度Goldstein-Lavintin-Polyak投影算法

给求解无约束规划问题的记忆梯度算法中的参数一个特殊取法,得到目标函数的记忆梯度G o ldste in-L av in tin-Po lyak投影下降方向,从而对凸约束的非线性规划问题构造了一个记忆梯度G o ldste in-L av in tin-Po lyak投影算法,并在一维精确步长搜索和去掉迭代点列有界的条件下,分析了算法的全局收敛性,得到了一些较为深刻的收敛性结果.同时给出了结合FR,PR,HS共轭梯度算法的记忆梯度G o ldste in-L av in tin-Po lyak投影算法,从而将经典共轭梯度算法推广用于求解凸约束的非线性规划问题.数值例子表明新算法比梯度投影算法有效.     

On the Determination of Convex Bodies by Projection Functions

This work is an investigation of the extent to which convexbodies are determined by the sizes of their projections. Itis shown, on the one hand, that there are non-congruent bodiesfor which all corresponding projections have the same size.On the other hand, it is proved that in the usual Baire categorysense, most convex bodies are determined, up to translationor reflection, by the combination of their widths and brightnessesin all directions. 1991 Mathematics Subject Classification 52A20.     

Subgradient Methods for Sharp Weakly Convex Functions

Subgradient methods converge linearly on a convex function that grows sharply away from its solution set. In this work, we show that the same is true for sharp functions that are only weakly convex, provided that the subgradient methods are initialized within a fixed tube around the solution set. A variety of statistical and signal processing tasks come equipped with good initialization and provably lead to formulations that are both weakly convex and sharp. Therefore, in such settings, subgradient methods can serve as inexpensive local search procedures. We illustrate the proposed techniques on phase retrieval and covariance estimation problems.     

Numerical Algorithm for Minimizing a Convex Function on the Intersection of a Smooth Surface and a Convex Compact Set

Computational Mathematics and Mathematical Physics - A numerical algorithm for minimizing a convex function on the set-theoretic intersection of a smooth surface and a convex compact set in...     

Sufficient Conditions for a Minimum of a Strongly Quasiconvex Function on a Weakly Convex Set

Mathematical Notes - We consider a finite-dimensional minimization problem for a strongly quasiconvex function on a weakly convex set. We obtain sufficient conditions for its solution expressed in...     

Hybrid Conjugate Gradient Method for a Convex Optimization Problem over the Fixed-Point Set of a Nonexpansive Mapping

The main aim of the paper is to accelerate the existing method for a convex optimization problem over the fixed-point set of a nonexpansive mapping. To achieve this goal, we present an algorithm (Algorithm 3.1) by using the conjugate gradient direction. We present also a convergence analysis (Theorem 3.1) under some assumptions. Finally, to demonstrate the effectiveness and performance of the proposed method, we present numerical comparisons of the existing method with the proposed method.     

在一个新步长规则下梯度投影算法的全局收敛性

考虑约束最优化问题：minx∈Ωf(x)其中:f:R^n→R是连续可微函数，Ω是一闭凸集。本文研究了解决此问题的梯度投影方法，在步长的选取时采用了一种新的策略，在较弱的条件下，证明了梯度投影响方法的全局收敛性。     

一类光滑凸规划的牛顿法

给出了一个求解一类光滑凸规划的算法,利用光滑精确乘子罚函数把一个光滑凸规划的极小化问题化为一个紧集上强凸函数的极小化问题,然后在给定的紧集上用牛顿法对这个强凸函数进行极小化．     

On a Set of Weakly Multiplicative Systems

Mathematical Notes -     

一类锥约束凸向量优化问题弱有效解集非空紧性的刻画和应用

本文研究当目标空间的控制结构为多面体锥时,锥约束凸向量优化问题的弱有效解集的非空紧性的刻画,然后将所获结果用于研究一类罚函数方法的收敛性.     

一类特殊凸对策的求解方法

针对支付函数对每个自变量都是严格凸函数的一类特殊凸对策问题，提出了求解局中人双方最优策略的一种简单方法。     

Uniform Continuity of a Family of Weakly Regular Set Functions on Topological Spaces

Conditions for the uniform continuity of a family of weakly regular set functions defined on an algebra of subsets of a -topological space (T,) and taking values in an arbitrary topological space are found.     

凸集图像重建问题的加速下松弛平行投影算法

平行投影算法是求解凸集图像重建问题的常用工具之一,它包括迭代复杂度O (1/k)收敛性的上松弛和下松弛两种形式.本文受Nesterov加速方法的启发,首先针对凸集图像重建问题提出一种加速的下松弛并行投影算法,并在某些合适的条件下证明了其迭代复杂度O(1/k2)的收敛性.然后又提出了一种基于Arimijo技术的自适应加速...     

Preconditioned Spectral Projected Gradient Method on Convex Sets

The spectral gradient method has proved to be effective for solving large-scale unconstrained optimization problems. It has been recently extended and combined with the projected gradient method for solving optimization problems on convex sets. This combination includes the use of nonmonotone line search techniques to preserve the fast local convergence. In this work we further extend the spectral choice of steplength to accept preconditioned directions when a good preconditioner is available. We present an algorithmthat combines the spectral projected gradient method with preconditioning strategies toincrease the local speed of convergence while keeping the global properties. We discuss implementation details for solving large-scale problems.