In this talk we will discuss low-complexity algorithms for solving large-scale convex optimization problems. Such algorithms include: gradient projection, proximal gradient, Iterative Shrinkage-Thresholding (ISTA), Nesterov's acceleration, and Alternating Direction Method of Multipliers (ADMM). The emphasis of the discussion will be placed on the convergence behavior of these algorithms.