An Inexact Perturbed Path-Following Method for Lagrangian Decomposition in Large-Scale Separable Convex Optimization
研究了一种不精确扰动路径跟踪算法，用于求解大规模可分凸规划问题，通过允许子问题不精确求解来降低计算成本，并分析了收敛性和复杂度。
This paper studies an inexact perturbed path-following algorithm in the framework of Lagrangian dual decomposition for solving large-scale separable convex programming problems. Unlike the exact versions considered in the literature, we propose solving the primal subproblems inexactly up to a given accuracy. This leads to an inexactness of the gradient vector and the Hessian matrix of the smoothed dual function. Then an inexact perturbed algorithm is applied to minimize the smoothed dual function. The algorithm consists of two phases, and both make use of the inexact derivative information of the smoothed dual problem. The convergence of the algorithm is analyzed, and the worst-case complexity is estimated. As a special case, an exact path-following decomposition algorithm is obtained and its worst-case complexity is given. Implementation details are discussed, and preliminary numerical results are reported.