Sparse Network Optimization for Synchronization

Avoiding Synchronization in First-Order Methods for Sparse Convex Optimization

Parallel computing has played an important role in speeding up convex optimization methods for big data analytics and large-scale machine learning (ML). However, the scalability of these optimization methods is inhibited by the cost of communicating and synchronizing processors in a parallel setting. Iterative ML methods are particularly sensitive to communication cost since they often require ...

Network Time Synchronization Using Clock Offset Optimization

Protocol synchronization with sparse timestraps

We present a family of transport protocols for transmitting messages from a sender process to a receiver process over a one-directional channel that may reorder, duplicate, or lose messages. In these protocols, each sent message is timestamped with the real-time of the sender when the message is sent. These timestamps are sparse, rather than uniform. We show how such timestamps, despite their s...

Sparse Optimization for Motion Segmentation

In this paper, we propose a new framework for segmenting feature-based multiple moving objects with subspace models in affine views. Since the feature data is high-dimensional and complex in the real video sequences, most traditional approaches for motion segmentation use the conventional PCA to obtain a low-dimensional representation, while our proposed framework applies the sparse PCA (SPCA) ...

Pathwise Coordinate Optimization for Sparse

The pathwise coordinate optimization is one of the most important computational frameworks for high dimensional convex and nonconvex sparse learning problems. It differs from the classical coordinate optimization algorithms in three salient features: warm start initialization, active set updating, and strong rule for coordinate preselection. Such a complex algorithmic structure grants superior ...