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We present a survey of nondifferentiable optimization problems and methods with special focus on the analytic center cutting plane method. We propose a self-contained convergence analysis that uses the formalism of the theory of self-concordant functions, but for the main results, we give direct proofs based on the properties of the logarithmic function. We also provide an in-depth analysis of two extensions that are very relevant to practical problems: the case of multiple cuts and the case of deep cuts.We further examine extensions to problems including feasible sets partially described by an explicit barrier function, and to the case of nonlinear cuts. Finally, we review several implementation issues and discuss some applications.

The relaxation method for solving systems of inequalities is related both to subgradient optimization and to the relaxation methods used in numerical analysis. The convergence theory depends upon two condition numbers. The first one is used mostly for the study of the rate of geometric convergence. The second is used to define a range of values of the relaxation parameter which guarantees finite convergence. In the case of obtuse polyhedra, finite convergence occurs for any value of the relaxation parameter between one and two. Various relationships between the condition numbers and the concept of obtuseness are established.

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