CORE MACHINE LEARNING
Neural Fixed-Point Acceleration
July 09, 2021
Abstract
Fixed-point iterations are at the heart of numerical computing and are often a computational bottleneck in real-time applications, which typically instead need a fast solution of moderate accuracy. Classical acceleration methods for fixed-point problems focus on designing algorithms with theoretical guarantees that apply to any fixed-point problem. We present neural fixed-point acceleration, a framework to automatically learn to accelerate convex fixed-point problems that are drawn from a distribution, using ideas from meta-learning and classical acceleration algorithms. We apply our framework to SCS, the state-of-the-art solver for convex cone programming, and design models and loss functions to overcome the challenges of learning over unrolled optimization and acceleration instabilities. Our work brings neural acceleration into any optimization problem expressible with CVXPY.
AUTHORS
Publisher
NeurIPS AutoML Workshop
Research Topics
Core Machine Learning
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