Summary of Congo: Compressive Online Gradient Optimization, by Jeremy Carleton et al.
CONGO: Compressive Online Gradient Optimization
by Jeremy Carleton, Prathik Vijaykumar, Divyanshu Saxena, Dheeraj Narasimha, Srinivas Shakkottai, Aditya Akella
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel framework called Compressive Online Gradient Optimization (CONGO) to address the challenge of zeroth-order online convex optimization, where the objective function’s gradient exhibits sparsity. This problem is motivated by the need to optimize large-scale queueing networks that process time-sensitive jobs while balancing latency and cost. The authors leverage this sparsity to obtain useful estimates of the objective function’s gradient even with limited function samples. CONGO achieves regret bounds with an optimal dependence on the time horizon, reducing the number of required samples per gradient estimate to scale with the gradient’s sparsity factor rather than its full dimensionality. Numerical simulations and real-world microservices benchmarks demonstrate CONGO’s superiority over traditional gradient descent approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a special kind of optimization problem where the information we have about an objective function is limited, but it’s useful because some parts of the function change quickly while others don’t change much. This happens when you’re trying to optimize how many resources to use in a big network of queues that process jobs. The goal is to find the right balance between how fast the jobs are processed and how much it costs to use the resources. The authors create a new way to do this called Compressive Online Gradient Optimization (CONGO) that uses special techniques to make the most of the limited information we have. It’s shown to work better than other methods in some simulations. |
Keywords
* Artificial intelligence * Gradient descent * Objective function * Optimization
