Optimality condition and complexity analysis for linearly-constrained optimization without differentiability on the boundary

• 成果类型: Article
• 署名作者: Haeser, Gabriel; Liu, Hongcheng; Ye, Yinyu
• 署名单位: Universidade de Sao Paulo; Stanford University; Stanford University
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-018-1290-4
• 发表日期: 2019
• 页码: 263-299
• 关键词: nonconcave penalized likelihood; nonlinear stepsize control; quadratic regularization; 2nd-order optimality; cubic-regularization; algorithmic theory; Newton method; M-ESTIMATORS; CONVERGENCE; regression

摘要

In this paper we consider the minimization of a continuous function that is potentially not differentiable or not twice differentiable on the boundary of the feasible region. By exploiting an interior point technique, we present first- and second-order optimality conditions for this problem that reduces to classical ones when the derivative on the boundary is available. For this type of problems, existing necessary conditions often rely on the notion of subdifferential or become non-trivially weaker than the KKT condition in the (twice-)differentiable counterpart problems. In contrast, this paper presents a new set of first- and second-order necessary conditions that are derived without the use of subdifferential and reduce to exactly the KKT condition when (twice-)differentiability holds. As a result, these conditions are stronger than some existing ones considered for the discussed minimization problem when only non-negativity constraints are present. To solve for these optimality conditions in the special but important case of linearly constrained problems, we present two novel interior point trust-region algorithms and show that their worst-case computational efficiency in achieving the potentially stronger optimality conditions match the best known complexity bounds. Since this work considers a more general problem than those in the literature, our results also indicate that best known existing complexity bounds are actually held for a wider class of nonlinear programming problems. This new development is significant since optimality conditions play a fundamental role in computational optimization and more and more nonlinear and nonconvex problems need to be solved in practice.