Structural and Multidisciplinary Optimization manuscript No. (will be inserted by the editor) pyOpt: A Python-Based Object-Oriented Framework for Nonlinear Constrained Optimization

Abstract We present pyOpt, an object-oriented frame-work for formulating and solving nonlinear constrained optimization problems in an efficient, reusable and portable manner. The framework uses object-oriented concepts, such as class inheritance and operator overloading, to maintain a distinct separation between the problem for-mulation and the optimization approach used to solve the problem. This creates a common interface in a flex-ible environment where both practitioners and devel-opers alike can solve their optimization problems or develop and benchmark their own optimization algo-rithms. The framework is developed in the Python pro-gramming language, which allows for easy integration of optimization software that is programmed in Fortran, C, C++, and other languages. A variety of optimization algorithms are integrated in pyOpt and are accessible through the common interface. We solve a number of problems of increasing complexity to demonstrate how a given problem is formulated using this framework, and how the framework can be used to benchmark the various optimization algorithms