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Nonlinearly constrained optimization problems are commonly solved using iterative methods. We present a double loop framework that allows us to express a broad range of state-of-the-art nonlinear optimization solvers within a common framework. The main components of this framework include strategies for computing a descent direction and mechanisms that promote global convergence. We start by introducing an abstract framework with four common ingredients that describes most nonlinear optimization methods and unifies their workflows. We then present Uno, a modern, lightweight and extensible C++ solver that unifies the workflow of most derivative-based iterative optimization methods. Uno is meant to enable researchers to experiment with novel optimization strategies while leveraging established subproblem solvers and interfaces to modeling languages. We demonstrate that Uno is competitive with state-of-the-art solvers, and illustrate its power by combining and extending existing solvers.