VOPy’s documentation!
Welcome to VOPy, an open-source Python library built to tackle the challenges of black-box vector optimization. Designed for scenarios where multiple objectives must be optimized simultaneously, VOPy goes beyond standard multi-objective optimization tools by offering a flexible ordering of solutions with respect to partial order relation. This order relation is encoded in a polyhedral convex cone to be input by the user. With features tailored for noisy environments, both discrete and continuous design spaces, limited budgets, and batch observations, VOPy opens up new possibilities for researchers and practitioners. Its modular architecture supports easy integration of existing methods and encourages the creation of innovative algorithms, making VOPy a versatile toolkit for advancing work in vector optimization.
Before starting: To better understand why you would do vector optimization and how VOPy is helpful, please first check our motivating example. You can then traverse through other examples to get a better understanding of VOPy.
Examples