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.

中文翻译：

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TSSOS：一个利用稀疏性进行大规模多项式优化的Julia库

Julia库TSSOS旨在帮助多项式优化器解决稀疏输入数据的大规模问题。底层的算法框架基于利用相关性和术语稀疏性来获得新的矩SOS层次结构，该层次结构可能包含更小的正半定矩阵。TSSOS可以应用于从电力网络到非交换多项式的特征值和迹线优化的众多问题，涉及多达数万个变量和约束。