Automatic source code generation for deterministic global optimization with parallel architectures

TL;DR: Finite representations Finite evaluation Finite convergence Computable sufficient conditions for existence and convergence Safe starting regions for iterative methods.

TL;DR: For nonlinear programming problems which are factorable, a computable procedure for obtaining tight underestimating convex programs is presented to exclude from consideration regions where the global minimizer cannot exist.

TL;DR: Automatic differentiation (AD) is a family of techniques similar to but more general than backpropagation for efficiently and accurately evaluating derivatives of numeric functions expressed as computer programs as discussed by the authors, which is a small but established field with applications in areas including computational uid dynamics, atmospheric sciences, and engineering design optimization.

TL;DR: Automatic differentiation (AD) is a family of techniques similar to backpropagation for efficiently and accurately evaluating derivatives of numeric functions expressed as computer programs as discussed by the authors, which is a small but established field with applications in areas including computational fluid dynamics, atmospheric sciences, and engineering design optimization.