Minimizing Compositions of Differences-of-Convex Functions with Smooth Mappings
研究了目标函数为凸差函数与光滑映射复合的最小化问题，提出了DC复合算法（DCCA）并分析其收敛性，应用于矩阵数值半径计算和复合能量最小化。
We address the so-called DC (difference-of-convex functions) composite minimization problems (or DC composite programs) whose objective function is a composition of a DC function with a continuously differentiable mapping. We first develop an algorithm named DC composite algorithm (DCCA in short) for unconstrained DC composite programs and further extend to DC composite programs with constraints of inclusion associated with a smooth mapping and a closed convex set. The convergence analysis of the proposed algorithms is investigated. Applications of DCCA for two different problems, computation of the numerical radius of a square matrix and minimization of composite energies, are presented.