Dynamic Energy Management

Moehle, Nicholas; Busseti, Enzo; Boyd, Stephen; Wytock, Matt

doi:10.48550/arxiv.1903.06230

Cited by 6 publications

(6 citation statements)

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“…ML could also be used to fit fast function approximators to existing optimization problems or to find good starting points for optimization. Dynamic scheduling [63,64] and safe reinforcement learning could also be used to balance the electric grid in real time to accommodate variable generation or demand; in fact, some electricity system operators have started to pilot similar methods at small, test case-based scales.…”

Section: High Leveragementioning

confidence: 99%

Tackling Climate Change with Machine Learning

Rolnick¹,

Donti²,

Kaack³

et al. 2019

Preprint

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Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

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Section: High Leveragementioning

confidence: 99%

Tackling Climate Change with Machine Learning

Rolnick¹,

Donti²,

Kaack³

et al. 2019

Preprint

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“…The list of applications of convex optimization is large and still growing. It has applications in vehicle control [SB08,Bla16,LB14], finance [CT06, BBD + 17], dynamic energy management [MBBW19], resource allocation [ABN + 21], machine learning [FHT01,BPC + 11], inverse design of physical systems [AVB21], circuit design [HBL01,BKPH05], and many other fields.…”

Section: Convex Analysis and Optimization Convex Analysis A Functionmentioning

confidence: 99%

Constant Function Market Makers: Multi-Asset Trades via Convex Optimization

Angeris¹,

Agrawal²,

Evans³

et al. 2021

Preprint

Self Cite

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The rise of Ethereum and other blockchains that support smart contracts has led to the creation of decentralized exchanges (DEXs), such as Uniswap, Balancer, Curve, mStable, and SushiSwap, which enable agents to trade cryptocurrencies without trusting a centralized authority. While traditional exchanges use order books to match and execute trades, DEXs are typically organized as constant function market makers (CFMMs). CFMMs accept and reject proposed trades based on the evaluation of a function that depends on the proposed trade and the current reserves of the DEX. For trades that involve only two assets, CFMMs are easy to understand, via two functions that give the quantity of one asset that must be tendered to receive a given quantity of the other, and vice versa. When more than two assets are being exchanged, it is harder to understand the landscape of possible trades. We observe that various problems of choosing a multi-asset trade can be formulated as convex optimization problems, and can therefore be reliably and efficiently solved.

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“…We consider a special case modeled on the DC power flow problem in power engineering [MBBW19]. The flow costs are quadratic with a capacity constraint:…”

Section: Single Commodity Flow Optimizationmentioning

confidence: 99%

Anderson Accelerated Douglas-Rachford Splitting

Fu,

Zhang,

Boyd

2019

Preprint

Self Cite

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We consider the problem of non-smooth convex optimization with general linear constraints, where the objective function is only accessible through its proximal operator. This problem arises in many different fields such as statistical learning, computational imaging, telecommunications, and optimal control. To solve it, we propose an Anderson accelerated Douglas-Rachford splitting (A2DR) algorithm, which we show either globally converges or provides a certificate of infeasibility/unboundedness under very mild conditions. Applied to a block separable objective, A2DR partially decouples so that its steps may be carried out in parallel, yielding an algorithm that is fast and scalable to multiple processors. We describe an open-source implementation and demonstrate its performance on a wide range of examples.

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Dynamic Energy Management

Cited by 6 publications

References 0 publications

Tackling Climate Change with Machine Learning

Tackling Climate Change with Machine Learning

Constant Function Market Makers: Multi-Asset Trades via Convex Optimization

Anderson Accelerated Douglas-Rachford Splitting

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