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Wireless spectrum is a limited and valuable resource for communications. However, wireless spectrum is known to be underutilized in spacial, temporal, and spectral domains. The dynamic spectrum access (DSA) of cognitive radio networks provides the capability to improve the spectrum efficiency by allowing secondary users to access the spectrum opportunistically without interfering primary users. The dynamic spectrum access is a joint channel allocation and power control problem with the objective to maximize the aggregated throughput of all secondary users. This problem is especially difficult in multi-channel multi-user cognitive radio networks. In the literature it is often formulated as a mixed integer nonlinear programming (MINLP) problem which is NP-hard. Therefore, some approximation methods are proposed to solve this problem, which lead to suboptimal solutions. In this paper we carefully reexamine the DSA problem, and prove that the original MINLP problem formulation is over-parameterized. We show that the DSA problem could be formulated as a nonlinear programming (NLP) problem without losing globally optimal objective function values. Moreover, the optimal solution to this NLP problem could be obtained by an interior point DSA optimization algorithm in polynomial time. Simulation results show that the proposed method performs better than other approximation methods do.