A new AI model learns to "think" longer on hard problems, achieving more robust reasoning and better generalization to novel, unseen tasks.

Helix Parallelism’ can process millions of words and support 32x more concurrent users. It’s a breakthrough, but is it useful ...

Course Description This course discusses basic convex analysis (convex sets, functions, and optimization problems), optimization theory (linear, quadratic, semidefinite, and geometric programming; ...

This note considers the distributed optimization problem on directed graphs with nonconvex local objective functions and the unknown network connectivity. A new adaptive algorithm is proposed to ...

Advanced AI-based techniques scale-up solving complex combinatorial optimization problems Date: June 10, 2024 Source: University of California - San Diego Summary: A framework based on advanced AI ...

We call these Graphs of Convex Sets (GCS). Many classical problems in graph theory are naturally generalized to GCS, yielding a new class of problems at the interface of combinatorial and convex ...

In the new paper Convexifying Transformers: Improving Optimization and Understanding of Transformer Networks, a Stanford University and Google Research team provides a solid theoretical analysis of ...

where \(\mathsf{G}(\cdot)\) is some convex operator and \(\mathcal{F}\) is as set of feasible input distributions. Examples of such an optimization problem include finding capacity in information ...