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- Stephen P. Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein
- Foundations and Trends in Machine Learning
- 2011

Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasingly important to be able to solve problems with a very large number of features or training examples. As a result, both the decentralized collection or… (More)

In this paper, we describe the embedded conic solver (ECOS), an interior-point solver for second-order cone programming (SOCP) designed specifically for embedded applications. ECOS is written in low footprint, single-threaded, library-free ANSI-C and so runs on most embedded platforms. The main interior-point algorithm is a standard primal-dual Mehrotra… (More)

- Matt Kraning, Eric Chu, Javad Lavaei, Stephen P. Boyd
- Foundations and Trends in Optimization
- 2014

We consider a network of devices, such as generators, fixed loads, deferrable loads, and storage devices, each with its own dynamic constraints and objective, connected by AC and DC lines. The problem is to minimize the total network objective subject to the device and line constraints over a time horizon. This is a large optimization problem with variables… (More)

- Sanjay Agrawal, Eric Chu, Vivek R. Narasayya
- SIGMOD Conference
- 2006

The area of automatic selection of physical database design to optimize the performance of a relational database system based on a <i>workload</i> of SQL queries and updates has gained prominence in recent years. Major database vendors have released automated physical database design tools with the goal of reducing the total cost of ownership. An important… (More)

This paper considers the problem of fitting regression models to historical fleet data with mixed effects, which arises in the context of statistical monitoring of data from a fleet (population) of similar units. A fleet-wide extension of the multivariable statistical process control approach is used to monitor for three different types of faults: a… (More)

- Brendan O'Donoghue, Eric Chu, Neal Parikh, Stephen P. Boyd
- J. Optimization Theory and Applications
- 2016

We introduce a first-order method for solving very large convex cone programs. Themethod uses an operator splittingmethod, the alternating directionsmethod of multipliers, to solve the homogeneous self-dual embedding, an equivalent feasibility problem involving finding a nonzero point in the intersection of a subspace and a cone. This approach has several… (More)

- Eric Chu, Jennifer L. Beckmann, Jeffrey F. Naughton
- SIGMOD Conference
- 2007

A "sparse" data set typically has hundreds or even thousands of attributes, but most objects have non-null values for only a small number of these attributes. A popular view about sparse data is that it arises merely as the result of poor schema design. In this paper, we argue that rather than being the result of inept schema design,storing a sparse data… (More)

- Eric Chu, Akanksha Baid, Xiaoyong Chai, AnHai Doan, Jeffrey F. Naughton
- SIGMOD Conference
- 2009

A common criticism of database systems is that they are hard to query for users uncomfortable with a formal query language. To address this problem, form-based interfaces and keyword search have been proposed; while both have benefits, both also have limitations. In this paper, we investigate combining the two with the hopes of creating an approach that… (More)

This paper describes a framework for generating easily verifiable code to solve convex optimization problems in embedded applications by transforming them into equivalent second-order cone programs. In embedded applications, it is critical to be able to verify code correctness, but it is also desirable to be able to rapidly prototype and deploy… (More)

We propose an approach to detecting anomalies from aircraft cruise flight data. The detection is based on a model learned from the historical data of a fleet of aircraft. For a variety of cruise flight conditions with and without turbulence, we validate the approach using a FOQA dataset generated by a NASA flight simulator. We identify a regression model… (More)