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CVXGEN is a software tool that takes a high level description of a convex optimization problem family, and automatically generates custom C code that compiles into a reliable, high speed solver for th...
This paper considers moving horizon estimation (MHE) approach to solution of staged quadratic programming (QP) problems. Using an insight into the constrained solution structure for the growing horizo...
We present an alternating augmented Lagrangian method for convex optimization problems where the cost function is the sum of two terms, one that is separable in the variable blocks, and a second that ...
We investigate the design of taper structures for coupling to slow-light modes of various photonic-crystal waveguides while taking into account parameter uncertainties inherent in practical fabricatio...
Accuracy at the Top     Accuracy at the Top       2015/7/9
We introduce a new notion of classification accuracy based on the top tau-quantile values of a scoring function, a relevant criterion in a number of problems arising for search engines. We de...
Generalized additive models are an effective regression tool, popular in the statistics literature, that provide an automatic extension of traditional linear models to nonlinear systems. We pres...
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. I...
Maximizing a Sum of Sigmoids     Maximizing  Sum  Sigmoids       2015/7/9
The problem of maximizing a sum of sigmoidal functions over a convex constraint set arises in many application areas. This objective captures the idea of decreasing marginal returns to investment, and...
We develop a simple operator splitting method for solving a primal conic optimization problem; we show that the iterates also solve the dual problem. The resulting algorithm is very simple to describe...
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...
This paper describes a general purpose method for solving convex optimization problems in a distributed computing environment. In particular, if the problem data includes a large linear operator or ma...
In this paper, we propose a distributed algorithm to solve the Security Constrained Optimal Power Flow (SC-OPF) Problem. We consider a network of devices, each with its own dynamic constraints and obj...
We formulate multi-input multi-output (MIMO) proportional-integral-derivative (PID) controller design as an optimization problem that involves nonconvex quadratic matrix inequalities. We propose a sim...
This paper describes Convex.jl, a convex optimization modeling framework in Julia. Convex.jl translates problems from a user-friendly functional language into an abstract syntax tree describing the pr...
Principal components analysis (PCA) is a well-known technique for approximating a data set represented by a matrix by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets c...

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