Distributed Optimization and Data Market Design
London, 2017
Category: Optimization
Overall Rating
Score Breakdown
- Latent Novelty Potential: 4/10
- Cross Disciplinary Applicability: 3/10
- Technical Timeliness: 2/10
- Obscurity Advantage: 4/5
Synthesized Summary
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These contributions are deeply tied to problem formulations with significant simplifying assumptions... that limit their direct relevance and actionable potential for the most pressing and complex modern distributed systems and data market challenges.
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While obscure, the technical constraints and model simplicity prevent this work from being a hidden gem for transformative research directions.
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This paper presents conceptually interesting ideas like applying local computation principles to distributed optimization and jointly considering purchasing and placement in data markets.
Optimist's View
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The core idea of applying Local Computation Algorithms to continuous distributed optimization (specifically convex programs/LPs) is presented as a novel bridge between theoretical computer science and networked control/optimization.
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LOCO aims to answer local queries about parts of the solution based only on communication within a small, localized neighborhood.
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The concept of a robust, communication-efficient mechanism for deriving consistent local pieces of a global solution is highly applicable to modern large-scale decentralized systems.
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Modern computational tools, including potentially graph neural networks or learned optimizers, could be used to learn or improve the construction of the "query sets" or the "online algorithm" simulation step.
Skeptic's View
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LOCO comes at the cost of providing only an approximation... rather than converging to the optimum like ADMM.
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The theoretical guarantees rely on worst-case adversarial analysis..., which may not reflect practical scenarios.
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The core theoretical result... relies on a sparsity assumption... leading to bounded degree in the dependency graph. This is a significant structural constraint that may not hold for many large-scale distributed optimization problems.
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Datum is presented as a heuristic for the general geo-distributed case, only proven optimal for the simplified single-data-center case... Its claimed "near-optimality"... is based solely on a specific, modeled case study with simplifying assumptions.
Final Takeaway / Relevance
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