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Structure-exploiting interior point methods. (English) Zbl 1455.65095
Grama, Ananth (ed.) et al., Parallel algorithms in computational science and engineering. Cham: Birkhäuser. Model. Simul. Sci. Eng. Technol., 63-93 (2020).
Summary: Interior point methods are among the most popular techniques for large-scale nonlinear optimization, owing to their intrinsic ability of scaling to arbitrarily large problem sizes. Their efficiency has attracted, in recent years, a lot of attention due to increasing demand for large-scale optimization in industry and engineering. General purpose nonlinear programming solvers implementing interior point methods provide a generic framework that embraces almost every optimal control problem provided that the nonlinear functions are smooth and that their first and possibly the second derivatives are also available. However, a large class of industrial and engineering problems possesses a particular structure motivating the development of structure-exploiting interior point methods. We present an interior point framework that exploits the intrinsic structure of large-scale nonlinear optimization problems, with the purpose of accelerating the solution both for single-core or multicore execution and massively parallel high-performance computing infrastructures. Since the overall performance of interior point methods relies heavily on scalable sparse linear algebra solvers, particular emphasis is given to the underlying algorithms for the distributed solution of the associated sparse linear systems obtained at each iteration from the linearization of the optimality conditions. The interior point algorithm is implemented in an object-oriented parallel solver and applied for the solution of large-scale optimal control problems solved on a daily basis for the secure transmission and distribution of electricity in modern power grids.
For the entire collection see [Zbl 1446.65003].
65K05 Numerical mathematical programming methods
90C30 Nonlinear programming
90C51 Interior-point methods
Full Text: DOI
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