Learning to optimize (L2O) stands at the intersection of traditional optimization and machine
learning, utilizing the capabilities of machine learning to enhance conventional optimization …

In many applications, a combinatorial problem must be repeatedly solved with similar, but
distinct parameters. Yet, the parameters $ w $ are not directly observed; only contextual data …

Most decentralized optimization algorithms are handcrafted. While endowed with strong
theoretical guarantees, these algorithms generally target a broad class of problems, thereby …

We present a probabilistic model for stochastic iterative algorithms with the use case of
optimization algorithms in mind. Based on this model, we present PAC-Bayesian …

Recent years have seen a growing interest in understanding acceleration methods through
the lens of ordinary differential equations (ODEs). Despite the theoretical advancements …

Towards designing learned optimization algorithms that are usable beyond their training
setting, we identify key principles that classical algorithms obey, but have up to now, not …

Convergence in learning-to-optimize is hardly studied, because conventional convergence
guarantees in optimization are based on geometric arguments, which cannot be applied …

In frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) systems,
the growing number of base station antennas leads to prohibitive feedback overhead for …

Solving constrained nonlinear programs (NLPs) is of great importance in various domains
such as power systems, robotics, and wireless communication networks. One widely used …

Solving the sum rate maximization problem for interference reduction in multi-cell multi-user
multiple-input multiple-output (MIMO) wireless communication systems has been …