Users Guide for SnadiOpt: A Package Adding Automatic Differentiation to Snopt
نویسندگان
چکیده
SnadiOpt is a package that supports the use of the automatic differentiation package ADIFOR with the optimization package Snopt. Snopt is a general-purpose system for solving optimization problems with many variables and constraints. It minimizes a linear or nonlinear function subject to bounds on the variables and sparse linear or nonlinear constraints. It is suitable for large-scale linear and quadratic programming and for linearly constrained optimization, as well as for general nonlinear programs. The method used by Snopt requires the first derivatives of the objective and constraint functions to be available. The SnadiOpt package allows users to avoid the timeconsuming and error-prone process of evaluating and coding these derivatives. Given Fortran code for evaluating only the values of the objective and constraints, SnadiOpt automatically generates the code for evaluating the derivatives and builds the relevant Snopt input files and sparse data structures.
منابع مشابه
A nonlinear optimization package for long-term hydrothermal coordination
Long-term hydrothermal coordination is one of the main problems to be solved by an electric utility. Its solution provides the optimal allocation of hydraulic, thermal and nuclear resources at the different intervals of the planning horizon. The purpose of the paper is two-fold. Firstly, it presents a new package for solving the hydrothermal coordination problem. The model implemented accuratel...
متن کاملForward-Mode Automatic Differentiation in Julia
We present ForwardDiff, a Julia package for forward-mode automatic differentiation (AD) featuring performance competitive with low-level languages like C++. Unlike recently developed AD tools in other popular high-level languages such as Python and MATLAB, ForwardDiff takes advantage of just-in-time (JIT) compilation to transparently recompile AD-unaware user code, enabling efficient support fo...
متن کاملHigh-level Interfaces for the Mad (matlab Automatic Differentiation) Package
Presently, the MAD Automatic Differentiation package for matlab comprises an overloaded implementation of forward mode AD via the fmad class. A key design feature of the fmad class is a separation of the storage and manipulation of directional derivatives into a separate derivvec class. Within the derivvec class, directional derivatives are stored as matrices (2-D arrays) allowing for the use o...
متن کاملMadness: a package for Multivariate Automatic Differentiation
The madness package provides a class for automatic differentiation of ‘multivariate’ operations via forward accumulation. By ‘multivariate,’ we mean the class computes the derivative of a vector or matrix or multidimensional array (or scalar) with respect to a scalar, vector, matrix, or multidimensional array. The primary intended use of this class is to support the multivariate delta method fo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- CoRR
دوره cs.MS/0106051 شماره
صفحات -
تاریخ انتشار 2001