Fetching the paper…
Reading the bibliography…
We show how to efficiently compute the derivative (when it exists) of the solution map of log-log convex programs (LLCPs).
“Least squares auto-tuning” To appear in American Control Conference
Shane Barratt and Stephen Boyd · 1904
Earlier work this paper cites.
“Deep declarative networks: A new hope”
Stephen Gould, Richard Hartley and Dylan Campbell · 1909
Earlier work this paper cites.
“Fitting a kalman smoother to data” To appear in Engineering Optimization
Shane Barratt and Stephen Boyd · 1910
Earlier work this paper cites.
“Learning convex optimization control policies”
Akshay Agrawal, Shane Barratt, Stephen Boyd and Bartolomeo Stellato · 1912
Earlier work this paper cites.
“Stochastic processes occurring in the theory of queues and their analysis by the method of the imbedded Markov chain”
David Kendall · 1953
Earlier work this paper cites.
“Programs for automatic differentiation for the machine BESM”, 1959
L. Beda, L. Korolev, N. Sukkikh and T. Frolova · 1959
Earlier work this paper cites.
“Geometric Programming—Theory and Application”
Richard Duffin, Elmor Peterson and Clarence Zener · 1967
Earlier work this paper cites.
“Nonlinear Programming: Sequential Unconstrained Minimization Techniques”
A. Fiacco and G. McCormick · 1968
Earlier work this paper cites.
“Sensitivity analysis for nonlinear programming using penalty methods”
Anthony Fiacco · 1976
Earlier work this paper cites.
“On sensitivity analysis in geometric programming”
John Dinkel and Gary Kochenberger · 1977
Earlier work this paper cites.
“Sensitivity analysis in geometric programming”
Ron Dembo · 1982
Earlier work this paper cites.
“The solution of the chemical equilibrium programming problem with generalized Benders decomposition”
Richard Clasen · 1984
Earlier work this paper cites.
“Sensitivity analysis in posynomial geometric programming”
Jerzy Kyparisis · 1988
Earlier work this paper cites.
“Sensitivity analysis in geometric programming: Theory and computations”
Jerzy Kyparisis · 1990
Earlier work this paper cites.
“Perturbation Analysis of Optimization Problems”, Springer Series in Operations Research
J. Bonnans and A. Shapiro · 2000
Earlier work this paper cites.
“Optimal design of a CMOS op-amp via geometric programming”
Maria Hershenson, Stephen Boyd and Thomas Lee · 2001
Earlier work this paper cites.
“Learning with differentiable perturbed optimizers”
Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert and Francis Bach · 2002
Earlier work this paper cites.
“Efficient nonlinear optimizations of queuing systems”
Mung Chiang, Arak Sutivong and Stephen Boyd · 2002
Earlier work this paper cites.
“Optimal power control in interference-limited fading wireless channels with outage-probability specifications”
S. Kandukuri and S. Boyd · 2002
Earlier work this paper cites.
“Convex Optimization”
Stephen Boyd and Lieven Vandenberghe · 2004
Earlier work this paper cites.
“Robust analog/RF circuit design with projection-based posynomial modeling”
Xin Li, Padmini Gopalakrishnan, Yang Xu and Lawrence Pileggi · 2004
Earlier work this paper cites.
“YALMIP: A toolbox for modeling and optimization in MATLAB”
Johan L\"ofberg · 2004
Earlier work this paper cites.
“ORACLE: Optimization with recourse of analog circuits including layout extraction”
Yang Xu, Lawrence Pileggi and Stephen Boyd · 2004
Cited alongside, same era.
“Digital circuit optimization via geometric programming”
Stephen Boyd, Seung-Jean Kim, Dinesh Patil and Mark Horowitz · 2005
Cited alongside, same era.
“Geometric programming for communication systems”
Mung Chiang · 2005
Cited alongside, same era.
“Disciplined convex programming”
Michael Grant, Stephen Boyd and Yinyu Ye · 2006
Cited alongside, same era.
“A tutorial on geometric programming”
Stephen Boyd, Seung-Jean Kim, Lieven Vandenberghe and Arash Hassibi · 2007
Cited alongside, same era.
“Power control by geometric programming”
Mung Chiang, Chee Tan, Daniel Palomar, Daniel O’neill and David Julian · 2007
Cited alongside, same era.
“Differentiable MPC for end-to-end planning and control”
Brandon Amos, Ivan Jimenez, Jacob Sacks, Byron Boots and J. Kolter · 2018
Later among the works it cites.
“A vehicle design and optimization model for on-demand aviation”
Arthur Brown and Wesley Harris · 2018
Later among the works it cites.
“End-to-end differentiable physics for learning and control”
Filipe de Avila Belbute-Peres, Kevin Smith, Kelsey Allen, Josh Tenenbaum and J. Kolter · 2018
Later among the works it cites.
“Compiling machine learning programs via high-level tracing”
R. Frostig, M. Johnson and C. Leary · 2018
Later among the works it cites.
“What game are we playing? End-to-end learning in normal and extensive form games”
Chun Ling, Fei Fang and J Kolter · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
“Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation”
A. Griewank and A. Walther · 2008
Cited alongside, same era.
“Implicit Functions and Solution Mappings”
Asen Dontchev and Ralph Rockafellar · 2009
Cited alongside, same era.
“ECOS: An SOCP solver for embedded systems”
Alexander Domahidi, Eric Chu and Stephen Boyd · 2013
Cited alongside, same era.
“CVX: MATLAB software for disciplined convex programming, version 2.1”, http://cvxr.com/cvx , 2014
Michael Grant and Stephen Boyd · 2014
Cited alongside, same era.
“Geometric programming for aircraft design optimization”
Warren Hoburg and Pieter Abbeel · 2014
Cited alongside, same era.
“Optimal resource allocation for network protection: A geometric programming approach”
Victor Preciado, Michael Zargham, Chinwendu Enyioha, Ali Jadbabaie and George Pappas · 2014
Cited alongside, same era.
Ali Saab, Edward Burnell and Warren Hoburg · 2018
Later among the works it cites.
“diffcp: differentiating through a cone program, version 1.0”, https://github.com/cvxgrp/diffcp , 2019
A. Agrawal, S. Barratt, S. Boyd, E. Busseti and W. Moursi · 2019
Later among the works it cites.
“Differentiable oonvex optimization layers”
Akshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond and J Kolter · 2019
Later among the works it cites.
“Differentiating through a cone program”
Akshay Agrawal, Shane Barratt, Stephen Boyd, Enzo Busseti and Walaa Moursi · 2019
Later among the works it cites.
“Disciplined geometric programming”
Akshay Agrawal, Steven Diamond and Stephen Boyd · 2019
Later among the works it cites.
“TensorFlow Eager: A multi-stage, Python-embedded DSL for machine learning”
Akshay Agrawal, Akshay Modi, Alexandre Passos, Allen Lavoie, Ashish Agarwal, Asim Shankar, Igor Ganichev, Josh Levenberg, Mingsheng Hong, Rajat Monga and Shanqing Cai · 2019
Later among the works it cites.
“Differentiable optimization-based modeling for machine learning”, 2019
Brandon Amos · 2019
Later among the works it cites.
“MOSEK optimization suite”, http://docs.mosek.com/9.0/intro.pdf , 2019
MOSEK ApS · 2019
Later among the works it cites.
“Solution refinement at regular points of conic problems”
Enzo Busseti, Walaa Moursi and Stephen Boyd · 2019
Later among the works it cites.
“Log-sum-exp neural networks and posynomial models for convex and log-log-convex data”
Giuseppe Calafiore, Stephane Gaubert and Corrado Possieri · 2019
Later among the works it cites.
“Don’t unroll adjoint: Differentiating SSA-form programs”
M. Innes · 2019
Later among the works it cites.
“Resource optimization of product development projects with time-varying dependency structure”
Masaki Ogura, Junichi Harada, Masako Kishida and Ali Yassine · 2019
Later among the works it cites.
“Geometric programming for optimal positive linear systems”
Masaki Ogura, Masako Kishida and James Lam · 2019
Later among the works it cites.
“PyTorch: An imperative style, high-performance deep learning library”
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein and Luca Antiga · 2019
Later among the works it cites.
“GPkit: A human-centered approach to convex optimization in engineering design”
Edward Burnell, Nicole Damen and Warren Hoburg · 2020
Closest in time.
“Coercing machine learning to output physically accurate results”
Zhenglin Geng, Daniel Johnson and Ronald Fedkiw · 2020
Closest in time.