Minimization methods for non-differentiable functions
Minimization methods for non-differentiable functions
More test examples for nonlinear programming codes
More test examples for nonlinear programming codes
C/C++ Users Journal
Uml and the Unified Process: Practical Object-Oriented Analysis and Design
Uml and the Unified Process: Practical Object-Oriented Analysis and Design
Test Examples for Nonlinear Programming Codes
Test Examples for Nonlinear Programming Codes
SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization
SIAM Journal on Optimization
The complex-step derivative approximation
ACM Transactions on Mathematical Software (TOMS)
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Python Essential Reference (3rd Edition) (Developer's Library)
Python Essential Reference (3rd Edition) (Developer's Library)
OPT++: An object-oriented toolkit for nonlinear optimization
ACM Transactions on Mathematical Software (TOMS)
Python for Scientific Computing
Computing in Science and Engineering
Extended ant colony optimization for non-convex mixed integer nonlinear programming
Computers and Operations Research
Python Scripting for Computational Science
Python Scripting for Computational Science
pyMDO: An Object-Oriented Framework for Multidisciplinary Design Optimization
ACM Transactions on Mathematical Software (TOMS)
F2PY: a tool for connecting Fortran and Python programs
International Journal of Computational Science and Engineering
Journal of Global Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A laminate parametrization technique for discrete ply-angle problems with manufacturing constraints
Structural and Multidisciplinary Optimization
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We present pyOpt, an object-oriented framework 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 formulation and the optimization approach used to solve the problem. This creates a common interface in a flexible environment where both practitioners and developers alike can solve their optimization problems or develop and benchmark their own optimization algorithms. The framework is developed in the Python programming language, which allows for easy integration of optimization software programmed in Fortran, C, C+驴+, 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.