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ORCIDStephen P. Boyd
Person information
- affiliation: Stanford University, USA
- award (2017): IEEE James H. Mulligan, Jr. Education Medal
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2020 – today
- 2024
[j142]Rafael Perez Martinez
, Masaya Iwamoto, Kelly Woo, Zhengliang Bian, Roberto Tinti, Stephen P. Boyd, Srabanti Chowdhury:
Compact Model Parameter Extraction via Derivative-Free Optimization. IEEE Access 12: 123224-123235 (2024)- [j141]Eric Luxenberg, Philipp Schiele, Stephen P. Boyd:
Robust Bond Portfolio Construction via Convex-Concave Saddle Point Optimization. J. Optim. Theory Appl. 201(3): 1089-1115 (2024) - [j140]Eric Luxenberg, Dhruv Malik, Yuanzhi Li, Aarti Singh, Stephen P. Boyd:
Specifying and Solving Robust Empirical Risk Minimization Problems Using CVXPY. J. Optim. Theory Appl. 202(3): 1158-1168 (2024) - [j139]Philipp Schiele, Eric Luxenberg, Stephen P. Boyd:
Disciplined Saddle Programming. Trans. Mach. Learn. Res. 2024 (2024) - [j138]Tobia Marcucci, Parth Nobel, Russ Tedrake, Stephen P. Boyd:
Fast Path Planning Through Large Collections of Safe Boxes. IEEE Trans. Robotics 40: 3795-3811 (2024) - [i51]Joshua Ott, Mykel J. Kochenderfer, Stephen P. Boyd:
Approximate Sequential Optimization for Informative Path Planning. CoRR abs/2402.08841 (2024) - [i50]Rafael Perez Martinez, Masaya Iwamoto, Kelly Woo, Zhengliang Bian, Roberto Tinti, Stephen P. Boyd, Srabanti Chowdhury:
Compact Model Parameter Extraction via Derivative-Free Optimization. CoRR abs/2406.16355 (2024) - [i49]Rafael Perez Martinez, Stephen P. Boyd, Srabanti Chowdhury:
Robust Pareto Design of GaN HEMTs for Millimeter-Wave Applications. CoRR abs/2406.17337 (2024) - 2023
- [j137]Bennet E. Meyers, Stephen P. Boyd:
Signal Decomposition Using Masked Proximal Operators. Found. Trends Signal Process. 17(1): 1-78 (2023) - [j136]Shane T. Barratt, Stephen P. Boyd:
Fitting feature-dependent Markov chains. J. Glob. Optim. 87(2): 329-346 (2023) - [j135]Parth Nobel, Akshay Agrawal, Stephen P. Boyd:
Computing tighter bounds on the n-queens constant via Newton's method. Optim. Lett. 17(5): 1229-1240 (2023) - [j134]Parth Nobel, Emmanuel J. Candès, Stephen P. Boyd:
Tractable Evaluation of Stein's Unbiased Risk Estimate With Convex Regularizers. IEEE Trans. Signal Process. 71: 4330-4341 (2023) - [c121]Maolin Wang, Ian McInerney, Bartolomeo Stellato, Stephen P. Boyd, Hayden Kwok-Hay So:
RSQP: Problem-specific Architectural Customization for Accelerated Convex Quadratic Optimization. ISCA 2023: 73:1-73:12 - [i48]Philipp Schiele, Eric Luxenberg, Stephen P. Boyd:
Disciplined Saddle Programming. CoRR abs/2301.13427 (2023) - [i47]Tobia Marcucci, Parth Nobel, Russ Tedrake, Stephen P. Boyd:
Fast Path Planning Through Large Collections of Safe Boxes. CoRR abs/2305.01072 (2023) - [i46]Ziheng Cheng, Junzi Zhang, Akshay Agrawal, Stephen P. Boyd:
Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified Models. CoRR abs/2305.02573 (2023) - [i45]Eric Luxenberg, Dhruv Malik, Yuanzhi Li, Aarti Singh, Stephen P. Boyd:
Specifying and Solving Robust Empirical Risk Minimization Problems Using CVXPY. CoRR abs/2306.05649 (2023) - [i44]Mehmet G. Ogut, Bennet E. Meyers, Stephen P. Boyd:
PV Fleet Modeling via Smooth Periodic Gaussian Copula. CoRR abs/2307.00004 (2023) - 2022
- [j133]Nir Shlezinger, Yonina C. Eldar, Stephen P. Boyd:
Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization. IEEE Access 10: 115384-115398 (2022) - [j132]Nicholas Moehle, Stephen P. Boyd:
A Certainty Equivalent Merton Problem. IEEE Control. Syst. Lett. 6: 1478-1483 (2022) - [j131]Maximilian Schaller, Goran Banjac, Steven Diamond, Akshay Agrawal, Bartolomeo Stellato, Stephen P. Boyd:
Embedded Code Generation With CVXPY. IEEE Control. Syst. Lett. 6: 2653-2658 (2022) - [j130]Guenther Walther, Alnur Ali, Xinyue Shen, Stephen P. Boyd:
Confidence Bands for a Log-Concave Density. J. Comput. Graph. Stat. 31(4): 1426-1438 (2022) - [j129]Akshay Agrawal, Stephen P. Boyd, Deepak Narayanan, Fiodar Kazhamiaka, Matei Zaharia:
Allocation of fungible resources via a fast, scalable price discovery method. Math. Program. Comput. 14(3): 593-622 (2022) - [j128]Anqi Fu, Lei Xing, Stephen P. Boyd:
Operator splitting for adaptive radiation therapy with nonlinear health dynamics. Optim. Methods Softw. 37(6): 2300-2323 (2022) - [j127]Shane T. Barratt, Stephen P. Boyd:
Stochastic Control With Affine Dynamics and Extended Quadratic Costs. IEEE Trans. Autom. Control. 67(1): 320-335 (2022) - [c120]Eric Luxenberg, Stephen P. Boyd, Misha van Beek, Wen Cao, Mykel J. Kochenderfer:
Strategic Asset Allocation with Illiquid Alternatives. ICAIF 2022: 249-256 - [c119]Guillermo Angeris, Alex Evans, Tarun Chitra, Stephen P. Boyd:
Optimal Routing for Constant Function Market Makers. EC 2022: 115-128 - [i43]Gabriel Maher, Stephen P. Boyd, Mykel John Kochenderfer, Cristian Matache, Alex Ulitsky, Slava Yukhymuk, Leonid Kopman:
A Light-Weight Multi-Objective Asynchronous Hyper-Parameter Optimizer. CoRR abs/2202.07735 (2022) - [i42]Bennet E. Meyers, Stephen P. Boyd:
Signal Decomposition Using Masked Proximal Operators. CoRR abs/2202.09338 (2022) - [i41]Nir Shlezinger, Yonina C. Eldar, Stephen P. Boyd:
Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization. CoRR abs/2205.02640 (2022) - [i40]Eric Luxenberg, Stephen P. Boyd, Mykel J. Kochenderfer, Misha van Beek, Wen Cao, Steven Diamond, Alex Ulitsky, Kunal Menda, Vidy Vairavamurthy:
Strategic Asset Allocation with Illiquid Alternatives. CoRR abs/2207.07767 (2022) - 2021
- [j126]Akshay Agrawal, Alnur Ali, Stephen P. Boyd:
Minimum-Distortion Embedding. Found. Trends Mach. Learn. 14(3): 211-378 (2021) - [j125]Akshay Agrawal, Shane T. Barratt, Stephen P. Boyd:
Learning Convex Optimization Models. IEEE CAA J. Autom. Sinica 8(8): 1355-1364 (2021) - [j124]Jonathan Tuck, Shane T. Barratt, Stephen P. Boyd:
A Distributed Method for Fitting Laplacian Regularized Stratified Models. J. Mach. Learn. Res. 22: 60:1-60:37 (2021) - [j123]Nicholas Moehle, Mykel J. Kochenderfer, Stephen P. Boyd, Andrew Ang:
Tax-Aware Portfolio Construction via Convex Optimization. J. Optim. Theory Appl. 189(2): 364-383 (2021) - [j122]Shane T. Barratt, Guillermo Angeris, Stephen P. Boyd:
Optimal representative sample weighting. Stat. Comput. 31(3): 19 (2021) - [j121]Charles W. Huang, Yong Yang, Neil Panjwani, Stephen P. Boyd, Lei Xing:
Pareto Optimal Projection Search (POPS): Automated Radiation Therapy Treatment Planning by Direct Search of the Pareto Surface. IEEE Trans. Biomed. Eng. 68(10): 2907-2917 (2021) - [j120]Steven Diamond, Vincent Sitzmann, Frank D. Julca-Aguilar, Stephen P. Boyd, Gordon Wetzstein, Felix Heide:
Dirty Pixels: Towards End-to-end Image Processing and Perception. ACM Trans. Graph. 40(3): 23:1-23:15 (2021) - [c118]Junzi Zhang, Jongho Kim, Brendan O'Donoghue, Stephen P. Boyd:
Sample Efficient Reinforcement Learning with REINFORCE. AAAI 2021: 10887-10895 - [c117]Deepak Narayanan, Fiodar Kazhamiaka, Firas Abuzaid, Peter Kraft, Akshay Agrawal, Srikanth Kandula, Stephen P. Boyd, Matei Zaharia:
Solving Large-Scale Granular Resource Allocation Problems Efficiently with POP. SOSP 2021: 521-537 - [i39]Shane T. Barratt, Yining Dong, Stephen P. Boyd:
Low Rank Forecasting. CoRR abs/2101.12414 (2021) - [i38]Shane T. Barratt, Stephen P. Boyd:
Covariance Prediction via Convex Optimization. CoRR abs/2101.12416 (2021) - [i37]Akshay Agrawal, Alnur Ali, Stephen P. Boyd:
Minimum-Distortion Embedding. CoRR abs/2103.02559 (2021) - [i36]Akshay Agrawal, Stephen P. Boyd, Deepak Narayanan, Fiodar Kazhamiaka, Matei Zaharia:
Allocation of Fungible Resources via a Fast, Scalable Price Discovery Method. CoRR abs/2104.00282 (2021) - [i35]Deepak Narayanan, Fiodar Kazhamiaka, Firas Abuzaid, Peter Kraft, Akshay Agrawal, Srikanth Kandula, Stephen P. Boyd, Matei Zaharia:
Solving Large-Scale Granular Resource Allocation Problems Efficiently with POP. CoRR abs/2110.11927 (2021) - 2020
- [j119]Mojtaba Tefagh, Stephen P. Boyd:
SWIFTCORE: a tool for the context-specific reconstruction of genome-scale metabolic networks. BMC Bioinform. 21(1): 140 (2020) - [j118]Reza Takapoui, Nicholas Moehle, Stephen P. Boyd, Alberto Bemporad:
A simple effective heuristic for embedded mixed-integer quadratic programming. Int. J. Control 93(1): 2-12 (2020) - [j117]Bartolomeo Stellato, Goran Banjac, Paul Goulart, Alberto Bemporad, Stephen P. Boyd:
OSQP: an operator splitting solver for quadratic programs. Math. Program. Comput. 12(4): 637-672 (2020) - [j116]Akshay Agrawal, Stephen P. Boyd:
Disciplined quasiconvex programming. Optim. Lett. 14(7): 1643-1657 (2020) - [j115]Shane T. Barratt, Guillermo Angeris, Stephen P. Boyd:
Minimizing a sum of clipped convex functions. Optim. Lett. 14(8): 2443-2459 (2020) - [j114]Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Stephen P. Boyd, Peter W. Glynn:
On the Convergence of Mirror Descent beyond Stochastic Convex Programming. SIAM J. Optim. 30(1): 687-716 (2020) - [j113]Junzi Zhang, Brendan O'Donoghue, Stephen P. Boyd:
Globally Convergent Type-I Anderson Acceleration for Nonsmooth Fixed-Point Iterations. SIAM J. Optim. 30(4): 3170-3197 (2020) - [j112]Anqi Fu, Junzi Zhang, Stephen P. Boyd:
Anderson Accelerated Douglas-Rachford Splitting. SIAM J. Sci. Comput. 42(6): A3560-A3583 (2020) - [c116]Shane T. Barratt, Stephen P. Boyd:
Fitting a Kalman Smoother to Data. ACC 2020: 1526-1531 - [c115]Jongho Kim, Youngsuk Park, John D. Fox, Stephen P. Boyd, William J. Dally:
Optimal Operation of a Plug-in Hybrid Vehicle with Battery Thermal and Degradation Model. ACC 2020: 3083-3090 - [c114]Youngsuk Park, Sauptik Dhar, Stephen P. Boyd, Mohak Shah:
Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize. ICASSP 2020: 3597-3601 - [c113]Akshay Agrawal, Shane T. Barratt, Stephen P. Boyd, Bartolomeo Stellato:
Learning Convex Optimization Control Policies. L4DC 2020: 361-373 - [c112]Malayandi Palan, Shane T. Barratt, Alex McCauley, Dorsa Sadigh, Vikas Sindhwani, Stephen P. Boyd:
Fitting a Linear Control Policy to Demonstrations with a Kalman Constraint. L4DC 2020: 374-383 - [i34]Jonathan Tuck, Stephen P. Boyd:
Eigen-Stratified Models. CoRR abs/2001.10389 (2020) - [i33]Jonathan Tuck, Stephen P. Boyd:
Fitting Laplacian Regularized Stratified Gaussian Models. CoRR abs/2005.01752 (2020) - [i32]Shane T. Barratt, Guillermo Angeris, Stephen P. Boyd:
Optimal Representative Sample Weighting. CoRR abs/2005.09065 (2020) - [i31]Akshay Agrawal, Shane T. Barratt, Stephen P. Boyd:
Learning Convex Optimization Models. CoRR abs/2006.04248 (2020) - [i30]Junzi Zhang, Jongho Kim, Brendan O'Donoghue, Stephen P. Boyd:
Sample Efficient Reinforcement Learning with REINFORCE. CoRR abs/2010.11364 (2020)
2010 – 2019
- 2019
- [j111]David Hallac, Peter Nystrup, Stephen P. Boyd:
Greedy Gaussian segmentation of multivariate time series. Adv. Data Anal. Classif. 13(3): 727-751 (2019) - [j110]Peter Nystrup, Stephen P. Boyd, Erik Lindström, Henrik Madsen:
Multi-period portfolio selection with drawdown control. Ann. Oper. Res. 282(1-2): 245-271 (2019) - [j109]Enzo Busseti, Walaa M. Moursi, Stephen P. Boyd:
Solution refinement at regular points of conic problems. Comput. Optim. Appl. 74(3): 627-643 (2019) - [j108]Jonathan Tuck, David Hallac, Stephen P. Boyd:
Distributed majorization-minimization for Laplacian regularized problems. IEEE CAA J. Autom. Sinica 6(1): 45-52 (2019) - [j107]Baris Ungun, Lei Xing, Stephen P. Boyd:
Real-Time Radiation Treatment Planning with Optimality Guarantees via Cluster and Bound Methods. INFORMS J. Comput. 31(3): 544-558 (2019) - [j106]Nicholas Moehle, Xinyue Shen, Zhi-Quan Luo, Stephen P. Boyd:
A Distributed Method for Optimal Capacity Reservation. J. Optim. Theory Appl. 182(3): 1130-1149 (2019) - [j105]Goran Banjac, Paul Goulart, Bartolomeo Stellato, Stephen P. Boyd:
Infeasibility Detection in the Alternating Direction Method of Multipliers for Convex Optimization. J. Optim. Theory Appl. 183(2): 490-519 (2019) - [j104]Akshay Agrawal, Steven Diamond, Stephen P. Boyd:
Disciplined geometric programming. Optim. Lett. 13(5): 961-976 (2019) - [j103]Shane T. Barratt, Mykel J. Kochenderfer, Stephen P. Boyd:
Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace From Position Data. IEEE Trans. Intell. Transp. Syst. 20(9): 3536-3545 (2019) - [c111]Akshay Agrawal, Brandon Amos, Shane T. Barratt, Stephen P. Boyd, Steven Diamond, J. Zico Kolter:
Differentiable Convex Optimization Layers. NeurIPS 2019: 9558-9570 - [i29]Shane T. Barratt, Stephen P. Boyd:
Least Squares Auto-Tuning. CoRR abs/1904.05460 (2019) - [i28]Jonathan Tuck, Shane T. Barratt, Stephen P. Boyd:
A Distributed Method for Fitting Laplacian Regularized Stratified Models. CoRR abs/1904.12017 (2019) - [i27]Akshay Agrawal, Stephen P. Boyd:
Disciplined Quasiconvex Programming. CoRR abs/1905.00562 (2019) - [i26]Dave Deriso, Stephen P. Boyd:
A General Optimization Framework for Dynamic Time Warping. CoRR abs/1905.12893 (2019) - [i25]Youngsuk Park, Sauptik Dhar, Stephen P. Boyd, Mohak Shah:
Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize. CoRR abs/1910.07056 (2019) - [i24]Akshay Agrawal, Brandon Amos, Shane T. Barratt, Stephen P. Boyd, Steven Diamond, J. Zico Kolter:
Differentiable Convex Optimization Layers. CoRR abs/1910.12430 (2019) - [i23]Akshay Agrawal, Shane T. Barratt, Stephen P. Boyd, Bartolomeo Stellato:
Learning Convex Optimization Control Policies. CoRR abs/1912.09529 (2019) - 2018
- [j102]Alberto Bemporad, Valentina Breschi, Dario Piga, Stephen P. Boyd:
Fitting jump models. Autom. 96: 11-21 (2018) - [j101]Akshay Agrawal, Robin Verschueren, Steven Diamond, Stephen P. Boyd:
A rewriting system for convex optimization problems. J. Control. Decis. 5(1): 42-60 (2018) - [j100]Jaehyun Park, Stephen P. Boyd:
A semidefinite programming method for integer convex quadratic minimization. Optim. Lett. 12(3): 499-518 (2018) - [j99]Steven Diamond, Reza Takapoui, Stephen P. Boyd:
A general system for heuristic minimization of convex functions over non-convex sets. Optim. Methods Softw. 33(1): 165-193 (2018) - [j98]Vincent Sitzmann, Steven Diamond, Yifan Peng, Xiong Dun, Stephen P. Boyd, Wolfgang Heidrich, Felix Heide, Gordon Wetzstein:
End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging. ACM Trans. Graph. 37(4): 114 (2018) - [c110]Valentina Breschi, Alberto Bemporad, Dario Piga, Stephen P. Boyd:
Prediction error methods in learning jump ARMAX models. CDC 2018: 2247-2252 - [c109]Bartolomeo Stellato, Vihangkumar V. Naik, Alberto Bemporad, Paul Goulart, Stephen P. Boyd:
Embedded Mixed-Integer Quadratic optimization Using the OSQP Solver. ECC 2018: 1536-1541 - [c108]Qingyun Sun, Mengyuan Yan, David L. Donoho, Stephen P. Boyd:
Convolutional Imputation of Matrix Networks. ICML 2018: 4825-4834 - [c107]David Hallac, Sagar Vare, Stephen P. Boyd, Jure Leskovec:
Toeplitz Inverse Covariance-based Clustering of Multivariate Time Series Data. IJCAI 2018: 5254-5258 - [i22]Ping Yin, Steven Diamond, Bill Lin, Stephen P. Boyd:
Network Optimization for Unified Packet and Circuit Switched Networks. CoRR abs/1808.00586 (2018) - [i21]Shane T. Barratt, Mykel J. Kochenderfer, Stephen P. Boyd:
Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace from Position Data. CoRR abs/1810.09568 (2018) - [i20]Akshay Agrawal, Steven Diamond, Stephen P. Boyd:
Disciplined Geometric Programming. CoRR abs/1812.04074 (2018) - 2017
- [j97]Stephen P. Boyd, Enzo Busseti, Steven Diamond, Ronald N. Kahn, Kwangmoo Koh, Peter Nystrup, Jan Speth:
Multi-Period Trading via Convex Optimization. Found. Trends Optim. 3(1): 1-76 (2017) - [j96]David Hallac, Christopher Wong, Steven Diamond, Abhijit Sharang, Rok Sosic, Stephen P. Boyd, Jure Leskovec:
SnapVX: A Network-Based Convex Optimization Solver. J. Mach. Learn. Res. 18: 4:1-4:5 (2017) - [j95]Nicholas Boyd, Trevor Hastie, Stephen P. Boyd, Benjamin Recht, Michael I. Jordan:
Saturating Splines and Feature Selection. J. Mach. Learn. Res. 18: 197:1-197:32 (2017) - [j94]Steven Diamond, Stephen P. Boyd:
Stochastic Matrix-Free Equilibration. J. Optim. Theory Appl. 172(2): 436-454 (2017) - [j93]Pontus Giselsson, Stephen P. Boyd:
Linear Convergence and Metric Selection for Douglas-Rachford Splitting and ADMM. IEEE Trans. Autom. Control. 62(2): 532-544 (2017) - [j92]Nan Zhang, Zhiqiang Yao, Yixian Liu, Stephen P. Boyd, Zhi-Quan Luo:
Dynamic Resource Allocation for Energy Efficient Transmission in Digital Subscriber Lines. IEEE Trans. Signal Process. 65(16): 4353-4366 (2017) - [c106]Youngsuk Park, David Hallac, Stephen P. Boyd, Jure Leskovec:
Learning the Network Structure of Heterogeneous Data via Pairwise Exponential Markov Random Fields. AISTATS 2017: 1302-1310 - [c105]Matt Wytock, Nicholas Moehle, Stephen P. Boyd:
Dynamic energy management with scenario-based robust MPC. ACC 2017: 2042-2047 - [c104]Goran Banjac, Bartolomeo Stellato, Nicholas Moehle, Paul Goulart, Alberto Bemporad, Stephen P. Boyd:
Embedded code generation using the OSQP solver. CDC 2017: 1906-1911 - [c103]David Hallac, Youngsuk Park, Stephen P. Boyd, Jure Leskovec:
Network Inference via the Time-Varying Graphical Lasso. KDD 2017: 205-213 - [c102]David Hallac, Sagar Vare, Stephen P. Boyd, Jure Leskovec:
Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data. KDD 2017: 215-223 - [c101]Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Stephen P. Boyd, Peter W. Glynn:
Stochastic Mirror Descent in Variationally Coherent Optimization Problems. NIPS 2017: 7040-7049 - [i19]Steven Diamond, Vincent Sitzmann, Stephen P. Boyd, Gordon Wetzstein, Felix Heide:
Dirty Pixels: Optimizing Image Classification Architectures for Raw Sensor Data. CoRR abs/1701.06487 (2017) - [i18]David Hallac, Youngsuk Park, Stephen P. Boyd, Jure Leskovec:
Network Inference via the Time-Varying Graphical Lasso. CoRR abs/1703.01958 (2017) - [i17]Nicholas Moehle, Xinyue Shen, Zhi-Quan Luo, Stephen P. Boyd:
A Distributed Method for Optimal Capacity Reservation. CoRR abs/1705.00677 (2017) - [i16]David Hallac, Sagar Vare, Stephen P. Boyd, Jure Leskovec:
Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data. CoRR abs/1706.03161 (2017) - [i15]Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Stephen P. Boyd, Peter W. Glynn:
Mirror descent in non-convex stochastic programming. CoRR abs/1706.05681 (2017) - [i14]Akshay Agrawal, Robin Verschueren, Steven Diamond, Stephen P. Boyd:
A Rewriting System for Convex Optimization Problems. CoRR abs/1709.04494 (2017) - [i13]Alberto Bemporad, Valentina Breschi, Dario Piga, Stephen P. Boyd:
Fitting Jump Models. CoRR abs/1711.09220 (2017) - 2016
- [j91]Madeleine Udell, Stephen P. Boyd:
Bounding duality gap for separable problems with linear constraints. Comput. Optim. Appl. 64(2): 355-378 (2016) - [j90]Madeleine Udell, Corinne Horn, Reza Zadeh, Stephen P. Boyd:
Generalized Low Rank Models. Found. Trends Mach. Learn. 9(1): 1-118 (2016) - [j89]Steven Diamond, Stephen P. Boyd:
CVXPY: A Python-Embedded Modeling Language for Convex Optimization. J. Mach. Learn. Res. 17: 83:1-83:5 (2016) - [j88]Weijie Su, Stephen P. Boyd, Emmanuel J. Candès:
A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights. J. Mach. Learn. Res. 17: 153:1-153:43 (2016) - [j87]Brendan O'Donoghue, Eric Chu, Neal Parikh, Stephen P. Boyd:
Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding. J. Optim. Theory Appl. 169(3): 1042-1068 (2016) - [j86]Thomas Lipp, Stephen P. Boyd:
Antagonistic control. Syst. Control. Lett. 98: 44-48 (2016) - [c100]Nicholas Moehle, Stephen P. Boyd:
Optimal current waveforms for switched-reluctance motors. CCA 2016: 1129-1136 - [c99]Reza Takapoui, Nicholas Moehle, Stephen P. Boyd, Alberto Bemporad:
A simple effective heuristic for embedded mixed-integer quadratic programming. ACC 2016: 5619-5625 - [c98]Xinyue Shen, Steven Diamond, Yuantao Gu, Stephen P. Boyd:
Disciplined convex-concave programming. CDC 2016: 1009-1014 - [c97]Pontus Giselsson, Mattias Fält, Stephen P. Boyd:
Line search for averaged operator iteration. CDC 2016: 1015-1022 - [c96]Nicholas Moehle, Stephen P. Boyd:
Maximum torque-per-current control of induction motors via semidefinite programming. CDC 2016: 1920-1925 - [c95]Nan Zhang, Zhiqiang Yao, Yixian Liu, Stephen P. Boyd, Zhi-Quan Luo:
Optimal Resource Allocation for Energy Efficient Transmission in DSL.