A Dual Simplex Algorithm for Piecewise-Linear Programming
提出一种对偶分段线性单纯形算法，用于在满足线性约束下最小化凸可分分段线性函数，相比间接方法更高效，并给出计算经验证明其优势。
This paper presents a dual piecewise-linear simplex algorithm for minimizing convex separable piecewise-linear functions subject to linear constraints. It is an extension of Fourier's work on piecewise-linear programming to the dual piecewise-linear simplex algorithm. This algorithm has advantages over indirect methods which solve equivalent linear programs augmented by additional variables and/or constraints. Computational experience is presented which demonstrates the efficiency these advantages contribute.