The security-constrained optimal power flow problem considers both the normal state and contingency constraints, and it is formulated as a large-scale nonconvex optimization problem. We propose a global optimization algorithm based on Lagrangian duality to solve the nonconvex problem to optimality. As usual, the global approach is often time-consuming, thus, for practical uses when dealing with a large number of contingencies, we investigate two decomposition algorithms based on Benders cut and the alternating direction method of multipliers. These decomposition schemes often generate solutions with a smaller objective function values than those generated by the conventional approach and very close to the globally optimal points. Index Terms-Alternating direction method of multipliers, Benders decomposition, branch-and-bound, Lagrangian duality, security-constrained optimal power flow.

We introduce a hybrid stochastic estimator to design stochastic gradient algorithms for solving stochastic optimization problems. Such a hybrid estimator is a convex combination of two existing biased and unbiased estimators and leads to some useful property on its variance. We limit our consideration to a hybrid SARAH-SGD for nonconvex expectation problems. However, our idea can be extended to handle a broader class of estimators in both convex and nonconvex settings. We propose a new single-loop stochastic gradient descent algorithm that can achieve O max σ 3 ε −1 , σε −3 -complexity bound to obtain an ε-stationary point under smoothness and σ 2 -bounded variance assumptions. This complexity is better than O σ 2 ε −4 often obtained in state-of-the-art SGDs when σ < O ε −3 . We also consider different extensions of our method, including constant and adaptive step-size with single-loop, double-loop, and mini-batch variants. We compare our algorithms with existing methods on several datasets using two nonconvex models.

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An exact algorithm is presented for solving edge weighted graph partitioning problems. The algorithm is based on a branch and bound method applied to a continuous quadratic programming formulation of the problem. Lower bounds are obtained by decomposing the objective function into convex and concave parts and replacing the concave part by an affine underestimate. It is shown that the best affine underestimate can be expressed in terms of the center and the radius of the smallest sphere containing the feasible set. The concave term is obtained either by a constant diagonal shift associated with the smallest eigenvalue of the objective function Hessian, or by a diagonal shift obtained by solving a semidefinite programming problem. Numerical results show that the proposed algorithm is competitive with state-of-the-art graph partitioning codes.AMS subject classifications. 90C35, 90C20, 90C27, 90C46