
Solving LargeScale Granular Resource Allocation Problems Efficiently with POP
Resource allocation problems in many computer systems can be formulated ...
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Operator Splitting for Adaptive Radiation Therapy with Nonlinear Health Dynamics
We present an optimizationbased approach to radiation treatment plannin...
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Allocation of Fungible Resources via a Fast, Scalable Price Discovery Method
We consider the problem of assigning or allocating resources to a set of...
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MinimumDistortion Embedding
We consider the vector embedding problem. We are given a finite set of i...
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Covariance Prediction via Convex Optimization
We consider the problem of predicting the covariance of a zero mean Gaus...
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Low Rank Forecasting
We consider the problem of forecasting multiple values of the future of ...
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Confidence bands for a logconcave density
We present a new approach for inference about a logconcave distribution...
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Sample Efficient Reinforcement Learning with REINFORCE
Policy gradient methods are among the most effective methods for larges...
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Learning Convex Optimization Models
A convex optimization model predicts an output from an input by solving ...
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Optimal Representative Sample Weighting
We consider the problem of assigning weights to a set of samples or data...
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Fitting Laplacian Regularized Stratified Gaussian Models
We consider the problem of jointly estimating multiple related zeromean...
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Convex Optimization Over RiskNeutral Probabilities
We consider a collection of derivatives that depend on the price of an u...
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EigenStratified Models
Stratified models depend in an arbitrary way on a selected categorical f...
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Learning Convex Optimization Control Policies
Many control policies used in various applications determine the input o...
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Differentiable Convex Optimization Layers
Recent work has shown how to embed differentiable optimization problems ...
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Minimizing a Sum of Clipped Convex Functions
We consider the problem of minimizing a sum of clipped convex functions;...
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Variable Metric Proximal Gradient Method with Diagonal BarzilaiBorwein Stepsize
Variable metric proximal gradient (VMPG) is a widely used class of conv...
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A General Optimization Framework for Dynamic Time Warping
Dynamic time warping (DTW) is a method that inputs two timedomain signa...
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Disciplined Quasiconvex Programming
We present a composition rule involving quasiconvex functions that gener...
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A Distributed Method for Fitting Laplacian Regularized Stratified Models
Stratified models are models that depend in an arbitrary way on a set of...
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Least Squares AutoTuning
Least squares is by far the simplest and most commonly applied computati...
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Disciplined Geometric Programming
We introduce loglog convex programs, which are optimization problems wi...
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Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace from Position Data
Models for predicting aircraft motion are an important component of mode...
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Network Optimization for Unified Packet and Circuit Switched Networks
Internet traffic continues to grow unabatedly at a rapid rate, driven la...
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CVXR: An R Package for Disciplined Convex Optimization
CVXR is an R package that provides an objectoriented modeling language ...
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A Rewriting System for Convex Optimization Problems
We describe a modular rewriting system for translating optimization prob...
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Dirty Pixels: Optimizing Image Classification Architectures for Raw Sensor Data
Realworld sensors suffer from noise, blur, and other imperfections that...
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Saturating Splines and Feature Selection
We extend the adaptive regression spline model by incorporating saturati...
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Convolutional Imputation of Matrix Networks
A matrix network is a family of matrices, where the relationship between...
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SnapVX: A NetworkBased Convex Optimization Solver
SnapVX is a highperformance Python solver for convex optimization probl...
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A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights
We derive a secondorder ordinary differential equation (ODE) which is t...
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Convex Optimization in Julia
This paper describes Convex, a convex optimization modeling framework in...
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Generalized Low Rank Models
Principal components analysis (PCA) is a wellknown technique for approx...
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An ADMM Algorithm for a Class of Total Variation Regularized Estimation Problems
We present an alternating augmented Lagrangian method for convex optimiz...
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Stephen Boyd
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Chair, Department of Electrical Engineering at Stanford University