HIGHLIGHTS
SUMMARY
Strong feasibility of primal and dual problems is a standard regularity condition in convex optimization, e_g_[24], Due to the advance of techniques of optimization modelling, there are many problems which do not satisfy primal-dual strong feasibility by nature. The authors demonstrate that, if perturbation is added only to the primal problem to recover strong feasibility, then the optimal value of the perturbed problem converges to the dual optimal value as the perturbation is reduced to zero, even in the presence of nonzero duality gap. The limiting optimal value is a function of . . .
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