  The Julia library TSSOS aims at helping polynomial optimizers to solve
large-scale problems with sparse input data. The underlying algorithmic
framework is based on exploiting correlative and term sparsity to obtain a new
moment-SOS hierarchy involving potentially much smaller positive semidefinite
matrices. TSSOS can be applied to numerous problems ranging from power networks
to eigenvalue and trace optimization of noncommutative polynomials, involving
up to tens of thousands of variables and constraints.
