Accelerated first-order methods for hyperbolic programming.

We develop a framework for applying accelerated methods to general hyperbolic programming, including linear, second-order cone, and semidefinite programming as special cases. The approach replaces a hyperbolic program with a convex optimization problem whose smooth objective function is explicit, and for which the only constraints are linear equations (one more linear equation than for the original problem). Virtually any first-order method can be applied. An iteration bound for a representative accelerated method is derived. [ABSTRACT FROM AUTHOR]

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