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It is becoming increasingly important to design high-performance circuits with as low power as possible. In this paper, we study the gate sizing and device parameter selection problem for today's industrial designs. We first outline the typical practical problems that make it difficult to use traditional algorithms on high-performance industrial designs. Then, we propose a Lagrangian relaxation-based formulation that decouples timing analysis from optimization without a resulting loss in accuracy. We also propose a graph model that accurately captures discrete cell-type characteristics based on library data. We model the relaxed Lagrangian subproblem as a graph problem and propose algorithms to solve it. In our experiments, we demonstrate the importance of using the signoff timing engine to guide the optimization. We also show the benefit of the graph model we propose to solve the discrete optimization problem. Compared to a state-of-the art industrial optimization flow, we show that our algorithms can obtain up to 38% leakage power reductions and better overall timing for real high-performance microprocessor blocks.