Disciplined Convex Programming

By Michael Grant, Stephen P. Boyd, Yinyu Ye. 2006

790 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computational Theory and Mathematics, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/e6d1fdcaaef71be1ae45ad34b54ed82f157507bc

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In Computational Mechanics

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Citations in the corpus

Disciplined convex-concave programming (2016, methodology): “convexication step fails. This is one of the issues we will address in this paper. 1.2 Disciplined convex programming Disciplined convex programming (DCP) is a methodology introduced by Grant et al. [GBY06] that imposes a set of conventions that must be followed when constructing (or specifying or d”
Graph Implementations for Nonsmooth Convex Programs (2008, background): “Disciplined convex programming was first named and described by Grant, Boyd, and Ye in [GBY06] and Grant in [Gra04].”
Noninvasive Transmural Electrophysiological Imaging Based on Minimization of Total-Variation Functional (2014, methodology): “First-order quadratic method fails to detect the infarct regions under all possible parameters using the cvx software mentioned in [18], [51].”
Cones and interior-point algorithms for structured convex optimization involving powers andexponentials (2009, background): “We want to point out here CVX, which is designed for so-called disciplined convex optimization problems (see [26]), i.”
Differentiable Convex Optimization Layers (2019, background): “At the foundation of these languages is a ruleset from convex analysis known as disciplined convex programming (DCP) [45].”
JuMP: A Modeling Language for Mathematical Optimization (2015, methodology): “CVX, based on the principle of disciplined convex programming (DCP) [40], allows users to express convex optimization problems in a specialized format which can be transformed into or approximated by conic programming without the need for computing derivatives.”
General Heuristics for Nonconvex Quadratically Constrained Quadratic Programming (2017, methodology): “le at https://github.com/cvxgrp/qcqp. 5.1 Quadratic expressions The CVXPYparser determines curvature, sign, and monotonicity of expressions according to the disciplined convex programming (DCP) rules [GBY06]. We have extended the parser so that it can also determine quadraticity of an expression, ba”
CVXGEN: a code generator for embedded convex optimization (2011, positive): “The language is built on the principles of disciplined convex programming (DCP) (Grant 2004; Grant et al. 2006; Grant and Boyd 2008b).”
Polyhedral approximation in mixed-integer convex optimization (2016, methodology): “In Section 4 we identify the modeling concept of disciplined convex programming (DCP) [23], popularized in the CVX software package [22], as a practical solution to the problem of automatically generating extended formulations based on a user’s algebraic representation of an MICP problem.”
A General-Purpose Software Framework for Dynamic Optimization (Een algemene softwareomgeving voor dynamische optimalisatie) (2013, methodology): “As was mentioned in Chapter 8, a possible extension of SCPgen is to implement the rules for disciplined convex programming [99].”
Convergent Fitted Value Iteration with Linear Function Approximation (2011, methodology): “36 seconds, whereas optimizing the op(2)-norm using the cvx package [10] took 8.”
Learning With Single-Teacher Multi-Student (2018, background): “(12). gSVM is a convex problem, which can be efficiently solved by many off-the-shelf toolboxes, such as mosek (Mosek 2010) and cvx (Grant, Boyd, and Ye 2008).”
CVXPY: A Python-Embedded Modeling Language for Convex Optimization (2016, methodology): “Like CVX, CVXPY uses disciplined convex programming (DCP) to verify problem convexity (Grant et al., 2006).”