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Fundamentals.- Aggregate subgradient methods for unconstrained convex minimization.- Methods with subgradient locality measures for minimizing nonconvex functions.- Methods with subgradient deletion rules for unconstrained nonconvex minimization.- Feasible point methods for convex constrained minimization problems.- Methods of feasible directions for nonconvex constrained problems.- Bundle methods.- Numerical examples.