Stable iterative Lagrange principle in convex programming as a tool for solving unstable problems

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• 作者：F. A. Kuterin ; M. I. Sumin
• 关键词：convex programming ; instability ; sequential optimization ; iterative dual regularization ; regularized Lagrange principle in iterative form ; unstable problems ; Fredholm integral equation of first kind.
• 刊名：Computational Mathematics and Mathematical Physics
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：57
• 期：1
• 页码：71-82
• 全文大小：
• 刊物类别：Mathematics and Statistics
• 刊物主题：Computational Mathematics and Numerical Analysis;
• 出版者：Pleiades Publishing
• ISSN：1555-6662
• 卷排序：57

文摘

A convex programming problem in a Hilbert space with an operator equality constraint and a finite number of functional inequality constraints is considered. All constraints involve parameters. The close relation of the instability of this problem and, hence, the instability of the classical Lagrange principle for it to its regularity properties and the subdifferentiability of the value function in the problem is discussed. An iterative nondifferential Lagrange principle with a stopping rule is proved for the indicated problem. The principle is stable with respect to errors in the initial data and covers the normal, regular, and abnormal cases of the problem and the case where the classical Lagrange principle does not hold. The possibility of using the stable sequential Lagrange principle for directly solving unstable optimization problems is discussed. The capabilities of this principle are illustrated by numerically solving the classical ill-posed problem of finding the normal solution of a Fredholm integral equation of the first kind.