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• W1966164803 abstract "Based on work originating in the early 1970s, a number of recent global optimization algorithms have relied on replacing an original nonconvex nonlinear program by convex or linear relaxations. Such linear relaxations can be generated automatically through an automatic differentiation process. This process decomposes the objective and constraints (if any) into convex and nonconvex unary and binary operations. The convex operations can be approximated arbitrarily well by appending additional constraints, while the domain must somehow be subdivided (in an overall branch-and-bound process or in some other local process) to handle nonconvex constraints. In general, a problem can be hard if even a single nonconvex term appears. However, certain nonconvex terms lead to easier-to-solve problems than others. Recently, Neumaier, Lebbah, Michel, ourselves, and others have paved the way to utilizing such techniques in a validated context.In this paper, we present a symbolic preprocessing step that provides a measure..." @default.
• W1966164803 created "2016-06-24" @default.
• W1966164803 creator A5021432838 @default.
• W1966164803 creator A5072151514 @default.
• W1966164803 date "2005-01-01" @default.
• W1966164803 modified "2023-09-25" @default.
• W1966164803 title "Validated Linear Relaxations and Preprocessing: Some Experiments" @default.
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• W1966164803 doi "https://doi.org/10.1137/030602186" @default.
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