This paper gives an overview of the majorization-minimization (MM) algorithmic framework,
which can provide guidance in deriving problem-driven algorithms with low computational …

Multiple-input multiple-output (MIMO) radars transmit a set of sequences that exhibit small
cross-correlation sidelobes, which enhance sensing performance by separating them at the …

Scene graph generation (SGG) aims to detect objects and predict the relationships between
each pair of objects. Existing SGG methods usually suffer from several issues, including 1) …

Outlier-robust estimation involves estimating some parameters (eg, 3D rotations) from data
samples in the presence of outliers, and is typically formulated as a non-convex and non …

Graph learning from data is a canonical problem that has received substantial attention in
the literature. Learning a structured graph is essential for interpretability and identification of …

Index tracking is a popular passive portfolio management strategy that aims at constructing a
portfolio that replicates or tracks the performance of a financial index. The tracking error can …

Learning a graph with a specific structure is essential for interpretability and identification of
the relationships among data. But structured graph learning from observed samples is an …

Recent years have witnessed a surge of interest in parallel and distributed optimization
methods for large-scale systems. In particular, nonconvex large-scale optimization problems …

Common spatial pattern (CSP) is one of the most successful feature extraction algorithms for
brain–computer interfaces (BCIs). It aims to find spatial filters that maximize the projected …

Resource allocation algorithms are conceived for minimizing the energy consumption of
multiuser mobile edge computing (MEC) systems operating in the face of interference …