Sequential Randomized Algorithms for Robust Convex Optimization


Sequential randomized algorithms are thought of for sturdy convex optimization which minimizes a linear objective function subject to a parameter dependent convex constraint. Using convex optimization and random sampling of parameter, these algorithms enable us to get a suboptimal resolution inside cheap computational time. The suboptimal solution is possible in a probabilistic sense and also the suboptimal value belongs to an interval which contains the optimal worth. The maximum of the interval is the optimal worth of the strong convex optimization and a specified tolerance. On the other hand, its minimum is the optimal worth of the prospect constrained optimization which could be a probabilistic relaxation of the sturdy convex optimization, with high likelihood.

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