作者: Yair Censor
DOI: 10.1088/1361-6420/33/4/044006
关键词:
摘要: Linear superiorization considers linear programming problems but instead of attempting to solve them with optimization methods it employs perturbation resilient feasibility-seeking algorithms and steers toward reduced (not necessarily minimal) target function values. The two questions that we set out explore experimentally are (i) Does provide a feasible point whose value is lower than obtained by running the same algorithm without under identical conditions? (ii) How does fare in comparison Simplex method for solving problems? Based on our computational experiments presented here, answers these are: "yes" "very well", respectively.