Source: adept-math Section: math Maintainer: Debian Math Team Uploaders: Pierre Gruet Build-Depends: debhelper-compat (= 13), gfortran, libblas-dev, liblapack-dev, libgsl-dev Standards-Version: 4.7.4 Vcs-Browser: https://salsa.debian.org/math-team/adept-math Vcs-Git: https://salsa.debian.org/math-team/adept-math.git Homepage: https://www.met.reading.ac.uk/clouds/adept/ Package: libadept0 Architecture: any Section: libs Depends: ${shlibs:Depends}, ${misc:Depends} Description: combined automatic differentiation and array library (shared library) Adept enables C++ algorithms to be automatically differentiated. For each mathematical statement involving scalars or arrays of a special "active" type, Adept stores the corresponding differential statement symbolically on a stack. The stack may then be used to perform the following computations: - Full Jacobian matrix. Given the non-linear function y=f(x) coded in C or C++, Adept will compute the matrix H, where the element at row i and column j of H is the partial derivative of y_i with respect to x_j. This matrix will be computed much more rapidly and accurately than if you simply recompute the function multiple times perturbing each element of x one by one. The Jacobian matrix is used in the Gauss-Newton and Levenberg-Marquardt minimization algorithms; - Reverse-mode differentiation. This is a key component in optimization problems where a non-linear function needs to be minimized but the state vector x is too large for it to make sense to compute the full Jacobian matrix. Atmospheric data assimilation is the canonical example in Meteorology. Given a non-linear function y=f(x) and a vector of adjoints, Adept will compute the vector of adjoints, without computing the full Jacobian matrix H. The adjoint may then be used in a quasi-Newton minimization scheme. - Forward-mode differentiation. Given the non-linear function y=f(x) and a vector of perturbations, Adept will compute the corresponding vector arising from a linearization of the function f. Formally, the perturbed output is given by the matrix-vector product, but it is computed here without computing the full Jacobian matrix H. Note that Adept is optimized for the reverse case, so might not be as fast (and will certainly not be as economical in memory) in the forward mode as libraries written especially for that purpose. . This package contains the shared libraries. Package: libadept-dev Architecture: any Section: libdevel Depends: libadept0 (= ${binary:Version}), ${shlibs:Depends}, ${misc:Depends} Description: combined automatic differentiation and array library (development files) Adept enables C++ algorithms to be automatically differentiated. For each mathematical statement involving scalars or arrays of a special "active" type, Adept stores the corresponding differential statement symbolically on a stack. The stack may then be used to perform the following computations: - Full Jacobian matrix. Given the non-linear function y=f(x) coded in C or C++, Adept will compute the matrix H, where the element at row i and column j of H is the partial derivative of y_i with respect to x_j. This matrix will be computed much more rapidly and accurately than if you simply recompute the function multiple times perturbing each element of x one by one. The Jacobian matrix is used in the Gauss-Newton and Levenberg-Marquardt minimization algorithms; - Reverse-mode differentiation. This is a key component in optimization problems where a non-linear function needs to be minimized but the state vector x is too large for it to make sense to compute the full Jacobian matrix. Atmospheric data assimilation is the canonical example in Meteorology. Given a non-linear function y=f(x) and a vector of adjoints, Adept will compute the vector of adjoints, without computing the full Jacobian matrix H. The adjoint may then be used in a quasi-Newton minimization scheme. - Forward-mode differentiation. Given the non-linear function y=f(x) and a vector of perturbations, Adept will compute the corresponding vector arising from a linearization of the function f. Formally, the perturbed output is given by the matrix-vector product, but it is computed here without computing the full Jacobian matrix H. Note that Adept is optimized for the reverse case, so might not be as fast (and will certainly not be as economical in memory) in the forward mode as libraries written especially for that purpose. . This package contains the static library and the header files.