Tentative list of topics for Term Paper (only sample papers are provided, others can also be
chosen, specify: theory/mixed/application mode)
1. Online learning and online convex optimization
Shai Shalev-Shwartz, Foundations and Trends in ML, 4(2),
2011
2. Introduction to Online optimization
Elad Hazan, Foundations and Trends in Optimization,
2(3-4), 2015
3. Proximal Algorithms
Neal Parikh and Stephen Boyd (2014), "Proximal
Algorithms", Foundations and Trends® in Optimization: Vol. 1: No.
3, pp 127-239
4. Multi-period investment
Stephen Boyd, Mark T. Mueller, Brendan O’Donoghue and
Yang Wang (2014), "Performance Bounds and Suboptimal Policies for
Multi–Period Investment", Foundations and Trends® in Optimization:
Vol. 1: No. 1
Stephen Boyd, Enzo Busseti, Steve Diamond, Ronald N.
Kahn, Kwangmoo Koh, Peter Nystrup and Jan Speth (2017),
"Multi-Period Trading via Convex Optimization", Foundations and
Trends® in Optimization: Vol. 3: No. 1
5. Improving ML and Cramer Rao bounds:
Yonina C. Eldar (2008), "Rethinking Biased Estimation:
Improving Maximum Likelihood and the Cramér–Rao Bound",
Foundations and Trends® in Signal Processing: Vol. 1: No. 4, pp
305-449.
6. ADMM
Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato and
Jonathan Eckstein (2011), "Distributed Optimization and
Statistical Learning via the Alternating Direction Method of
Multipliers", Foundations and Trends® in Machine Learning: Vol. 3:
No. 1,
7. Submodularity and convexity
Francis Bach (2013), "Learning with Submodular Functions:
A Convex Optimization Perspective", Foundations and Trends® in
Machine Learning: Vol. 6: No. 2-3.
8. Learning over Networks
Ali H. Sayed (2014), "Adaptation, Learning, and
Optimization over Networks", Foundations and Trends® in Machine
Learning: Vol. 7: No. 4-5, pp 311-801.
9. S-lemma
Polik, I., Terlaky T., A Survey on the S-Lemma , SIAM
Review Vol. 49, No. 3, pp. 371-418, 2004.
Derinkuyu, K., Pinar, M., On the S-Procedure and Some
Variants , 2004.
10. Approximate dynamic programming in transportation/logistics
W. B. Powell, H. Simao, B. Bouzaiene-Ayari, “Approximate
Dynamic Programming in Transportation and Logistics: A Unified
Framework,” European J. on Transportation and Logistics, Vol. 1,
No. 3, pp. 237-284 (2012). DOI 10.1007/s13676-012-0015-8.
11. Sequential stochastic optimization, dynamic programming
http://castlelab.princeton.edu/html/Papers/Powell-UnifiedFrameworkStochasticOptimization_July222017.pdf
Clearing the Jungle of Stochastic Optimization, Informs
Tutorials in Operations Research: Bridging Data and Decisions, pp.
109-137, November, 2014
12. Robust optimization in logistics (sample papers only, others
can be used):
Bertsimas D., Thiele A. (2004) A Robust Optimization
Approach to Supply Chain Management. In: Bienstock D., Nemhauser G.
(eds) Integer Programming and Combinatorial Optimization. IPCO 2004.
Lecture Notes in Computer Science, vol 3064. Springer, Berlin,
Heidelberg
Bertsimas, Dimitris, and Aurélie Thiele. "A robust
optimization approach to inventory theory." Operations research
54.1 (2006): 150-168.
Ben-Tal, Aharon, et al. "Retailer-supplier flexible
commitments contracts: A robust optimization approach." Manufacturing
& Service Operations Management 7.3 (2005): 248-271.
Bertsimas, Dimitris, and Melvyn Sim. "Tractable
approximations to robust conic optimization problems." Mathematical
programming 107.1-2 (2006): 5-36.
13. Subgradient methods
S. Boyd and A. Mutapcic, Subgradient Methods , Notes for
EE364b, Stanford University, 2006. Available online.
N. Shor, Minimization Methods for Non-Differentiable
Functions , Springer Series in Computational Mathematics, Springer,
1985.
D. Bertsekas, Nonlinear Programming. Athena Scientific,
1999.
14. Robust Optimization
Bertsimas, Dimitris, David B. Brown, and Constantine
Caramanis. "Theory and applications of robust optimization." SIAM
review 53.3 (2011): 464-501.
15. Robust PCA
Candès, Emmanuel J., et al. "Robust principal component
analysis?." Journal of the ACM (JACM) 58.3 (2011): 11.
Liu, Guangcan, et al.
"Robust recovery of subspace structures by low-rank
representation."
IEEE Transactions on Pattern Analysis and
Machine Intelligence 35.1 (2013): 171-184.
Wright, John, et al.
"Robust principal component analysis: Exact recovery of
corrupted low-rank matrices via convex optimization." Advances
in neural information processing systems. 2009.
Xu, Huan, Constantine Caramanis, and Sujay Sanghavi.
"Robust PCA via outlier pursuit." Advances in Neural
Information Processing Systems. 2010.
16. Matrix Completion
Cai, Jian-Feng, Emmanuel J. Candès, and Zuowei Shen.
"A singular value thresholding algorithm for matrix
completion." SIAM Journal on Optimization 20.4 (2010):
1956-1982.
Candès, Emmanuel J., and Benjamin Recht. "Exact matrix
completion via convex optimization." Foundations of
Computational mathematics 9.6 (2009): 717.
Lin, Zhouchen, Minming Chen, and Yi Ma. "The augmented
lagrange multiplier method for exact recovery of corrupted
low-rank matrices." arXiv preprint arXiv:1009.5055
(2010).
Candès, Emmanuel J., and Terence Tao. "The power of
convex relaxation: Near-optimal matrix completion." IEEE
Transactions on Information Theory 56.5 (2010):
2053-2080.
17. Coordinate ascent/descent
Yu. Nesterov, Efficiency of coordinate descent methods on
huge-scale optimization problems, SIAM Journal on Optimization,
2012
P. Richt ́arik and M. Takac, Iteration complexity of
randomized block-coordinate descent methods for minimizing a
composite function, Mathematical Programming, 2014
P. Tseng and S. Yun, A coordinate gradient descent method for
nonsmooth separable minimization, Mathematical Programming, 2009
18. Large-scale optimization
Feng Niu, Benjamin Recht, Christopher Re, and Stephen Wright.
Hogwild: A lock-free approach to parallelizing stochastic gradient
descent. In Advances in neural information processing systems, pages
693–701, 2011
Gemulla, Rainer, et al.
"Large-scale matrix factorization with distributed stochastic
gradient descent." Proceedings of the 17th ACM SIGKDD
international conference on Knowledge discovery and data mining.
ACM, 2011.
Bottou, Léon. "Large-scale machine learning with
stochastic gradient descent." Proceedings of COMPSTAT'2010.
Physica-Verlag HD, 2010. 177-186.
19. Semi-stochastic methods:
Konecný, Jakub, and Peter Richtárik. "Semi-stochastic gradient
descent methods." arXiv preprint arXiv:1312.1666 (2013).
Konečný, Jakub, et al.
"Mini-batch semi-stochastic gradient descent in the proximal
setting."
IEEE Journal of Selected Topics in Signal Processing
10.2 (2016): 242-255.
Konečný, Jakub, Zheng Qu,
and Peter Richtárik. "Semi-stochastic coordinate descent."
Optimization
Methods and Software 32.5 (2017): 993-1005.
Konecný, Jakub, Zheng Qu,
and Peter Richtárik. "S2cd: Semi-stochastic coordinate
descent." NIPS Optimization in Machine Learning workshop.
2014.
20. Stochastic and online optimization
A. Nemirovski, A. Juditsky, G. Lan and A. Shapiro,Robust
stochastic approximation approach to stochastic programming, SIAM
Journal on Optimization (2009)
Yu. Nesterov, Primal-dual subgradient methods for convex
problems, Mathematical Programming (2009)
L. Xiao, Dual averaging methods for regularized stochastic
learning and online optimization, Journal of Machine Learning
Research (2010)
N. Le Roux, M. Schmidt and F. Bach, A stochastic gradient
method with an exponential convergence rate for strongly convex
optimization with finite training sets, NIPS (2012)
21. Variance reduced stochastic gradient descent
R. Johnson and T. Zhang, Accelerating stochastic gradient
descent using predictive variance reduction NIPS (2013)
L. Xiao and T. Zhang, A proximal stochastic gradient method
with progressive variance reduction
Defazio, Aaron, Francis Bach, and Simon Lacoste-Julien.
"SAGA: A fast incremental gradient method with support for
non-strongly convex composite objectives." Advances in neural
information processing systems. 2014.
Schmidt, Mark, Nicolas Le Roux, and Francis Bach.
"Minimizing finite sums with the stochastic average gradient." Mathematical
Programming 162.1-2 (2017): 83-112.
22. Interior point methods and self-concordant analysis
S. Boyd and L. Vandenberghe, Convex Optimization (2004),
Chapter 9
23. Proximal point algorithms:
O. G ̈uler, On the convergence of the proximal point
algorithm for convex minimization, SIAM J. Control and
Optimization (1991)
O. G ̈uler,New proximal point algorithms for convex
minimization, SIOPT (1992)
O. G ̈uler, Augmented Lagrangian algorithm for linear
programming, JOTA (1992)
24. ADMM variants
D. Goldfarb, S. Ma, K. Scheinberg, Fast alternating
linearization methods for minimizing the sum of two convex
functions, (2010)
T. Goldstein and S. Osher, The split Bregman method for
L1-regularized problems, SIAM J. Imag. Sciences (2009)
25. Dual decomposition:
F. Kelly, A. Maulloo, D. Tan, Rate control in communication
networks: shadow prices, proportional fairness and stability, J.
Operation Research Society, 49 (1998).
A. Beck and M. Teboulle, Fast gradient-based algorithms for
constrained total variation image denoising and deblurring
problems, IEEE Transactions on Image Processing (2009)
26. Subgradient methods
Nedic, Angelia, and Asuman Ozdaglar. "Distributed subgradient
methods for multi-agent optimization." IEEE Transactions on
Automatic Control 54.1 (2009): 48-61.
Ram, S. Sundhar, Angelia Nedić, and Venugopal V. Veeravalli.
"Distributed stochastic subgradient projection algorithms for convex
optimization." Journal of optimization theory and applications
147.3 (2010): 516-545.
Tsitsiklis, John, Dimitri Bertsekas, and Michael Athans.
"Distributed asynchronous deterministic and stochastic gradient
optimization algorithms." IEEE transactions on automatic control
31.9 (1986): 803-812.
27. Smoothing
Yu. Nesterov, Smooth minimization of non-smooth functions,
Mathematical Programming (2005).
Yu. Nesterov, Excessive gap technique in nonsmooth convex
minimization, SIAM Journal on Optimization (2005)
28. Majorization Minimization (Palomar)
Sun, Ying, Prabhu Babu, and Daniel P. Palomar.
"Majorization-minimization algorithms in signal processing,
communications, and machine learning." IEEE Transactions on
Signal Processing 65.3 (2017): 794-816.
Figueiredo, Mário AT, José M. Bioucas-Dias, and Robert D.
Nowak. "Majorization–minimization algorithms for wavelet-based image
restoration." IEEE Transactions on Image processing 16.12
(2007): 2980-2991.
Mairal, Julien. "Incremental majorization-minimization
optimization with application to large-scale machine learning." SIAM
Journal on Optimization 25.2 (2015): 829-855.
29. l1 norm minimization algorithms
Yang, Allen Y., et al. "Fast ℓ 1-minimization algorithms and
an application in robust face recognition: A review." Image
Processing (ICIP), 2010 17th IEEE International Conference on.
IEEE, 2010.
Beck, Amir, and Marc Teboulle. "A fast iterative
shrinkage-thresholding algorithm for linear inverse problems." SIAM
journal on imaging sciences 2.1 (2009): 183-202.
30. Convex optimization in Finance
Yiyong Feng and Daniel P. Palomar, A
Signal Processing Perspective on Financial Engineering, Foundations
and Trends® in Signal Processing, Now Publishers, vol. 9, no. 1-2,
2016.
31. Blind deconvolution (Ken Ma
Ahmed, Ali, Benjamin Recht,
and Justin Romberg. "Blind deconvolution using convex
programming."
IEEE Transactions on Information Theory 60.3
(2014): 1711-1732.
Chan, Tsung-Han, et al. "A
convex analysis framework for blind separation of non-negative
sources."
IEEE Transactions on Signal Processing 56.10
(2008): 5120-5134.
Gillis, Nicolas, and
Stephen A. Vavasis. "Fast and robust recursive algorithmsfor
separable nonnegative matrix factorization." IEEE
transactions on pattern analysis and machine intelligence
36.4 (2014): 698-714.
32. Network utility maximization (Palomar Chiang)
Palomar, Daniel Pérez, and Mung Chiang. "A tutorial on
decomposition methods for network utility maximization."
IEEE
Journal on Selected Areas in Communications 24.8 (2006):
1439-1451.
Palomar, Daniel P., and Mung Chiang. "Alternative
distributed algorithms for network utility maximization: Framework
and applications."
IEEE Transactions on Automatic Control
52.12 (2007): 2254-2269.
33. Congestion control
Steven H. Low and David E. Lapsley,
“Optimization Flow Control, I: Basic Algorithm and
Convergence,” IEEE/ACM Trans. on Networking, vol. 7, no. 6,
Dec. 1999.
Steven H. Low,
Larry L. Peterson, and Liming Wang,
“Understanding Vegas: a duality model,” Journal of the ACM,
vol. 49, no. 2, March 2002.
Steven H. Low,
Fernando Paganini, and John C.
Doyle, “Internet Congestion Control,” IEEE Control
Systems Magazine, Feb. 2002.
Steven H. Low, “A Duality Model of TCP and Queue
Management Algorithms,” IEEE/ACM Trans. on Networking, Oct. 2003.
34. Segmentation and multiview 3D reconstruction in image
processing
Daniel Cremers, Thomas Pock, Kalin Kolev, and Antonin
Chambolle, “Convex Relaxation Techniques for Segmentation, Stereo
and Multiview Reconstruction” In Markov Random Fields for Vision and
Image Processing. MIT Press, 2011.
Antonin Chambolle, Daniel Cremers, and Thomas Pock,“A convex
approach to minimal partitions” SIAM Journal on Imaging Sciences.
5(4):1113–1158, 2012
Kalin Kolev, Maria Klodt, Thomas Brox, and Daniel
Cremers,“Continuous global optimization in multiview 3d
reconstruction,” International Journal of Computer Vision.
84(1):80–96, 2009.
35. MIMO linear transceiver design based on Schur convexity
Daniel P. Palomar, “A Unified Framework for
Communications through MIMO Channels,” Ph.D. dissertation,
Technical University of Catalonia, Barcelona, Spain, May 2003.
Daniel P. Palomar, “Convex Primal Decomposition for
Multicarrier Linear MIMO Transceivers,” IEEE Trans. on Signal
Processing, Vol. 53, No. 12, pp. 4661-4674, Dec. 2005.
Daniel P. Palomar and Javier Fonollosa, “Algorithms for a
Family of Waterfilling Solutions,” IEEE Trans. on Signal
Processing, Vol. 53, No. 2, pp. 686-695, Feb. 2005.
36. Iterative Waterfilling
W. Yu, W. Rhee, S. Boyd, and J. Cioffi, “Iterative
Water-Filling for Gaussian Vector Multiple-Access Channels,” IEEE
Trans. on Information Theory , vol. 50, no. 1, Jan. 2004.
W. Yu, “A Dual Decomposition Approach to the Sum Power
Gaussian Vector Multiple Access Channel Sum Capacity Problem,” in
Proc. Conf. on Information and Systems (CISS), The Johns Hopkins
Univ., March 12-14, 2003.
N. Jindal, W. Rhee, S. Vishwanath, S. A. Jafar, and A.
Goldsmith, “Sum Power Iterative Water-Filling for Multi-Antenna
Gaussian Broad cast Channels,” IEEE Trans. on Information Theory ,
vol. 51, no. 4, April 2005.
Daniel P. Palomar, “Convex Primal Decomposition for
Multicarrier Linear MIMO Transceivers,” IEEE Trans. on Signal
Processing , vol. 53, no. 12, Dec. 2005.
37. Minimax approaches in MIMO
Daniel P. Palomar, John M. Cioffi, and Miguel A. Lagunas,
“Uniform Power Allocation in MIMO Channels: A Game-Theoretic
Approach,” IEEE Trans. on Information Theory, vol. 49, no. 7,
July 2003.
Antonio Pascual-Iserte, Daniel P. Palomar, Ana P
́erez-Neira, Miguel A. Lagunas, “A Robust Maximim Approach for
MIMO Comm. with Imperfect CSI Based on Convex Optimization,” IEEE
Trans. on Signal Processing, vol. 54, no. 1, Jan. 2006.
Jiaheng Wang and Daniel P. Palomar, “Worst-Case Robust
Transmission in MIMO Channels with Imperfect CSIT,” IEEE Trans.
on Signal Processing , vol. 57, no. 8, pp. 3086-3100, Aug.
2009.
38. Game theoretic communication system design
Gesualdo Scutari, Daniel P. Palomar, and Sergio Barbarossa,
“Competitive Design of Multiuser MIMO Systems based on Game
Theory: A Unified View,” IEEE JSAC: Special Issue on Game Theory ,
vol. 25, no. 7, Sept. 2008.
Gesualdo Scutari, Daniel P. Palomar, and Sergio Barbarossa,
“Optimal Linear Precoding Strategies for Wideband Noncooperative
Systems Based on Game Theory,” IEEE Trans. on Signal Processing ,
vol. 56, no. 3, March 2008.
Gesualdo Scutari, Daniel P. Palomar, and Sergio Barbarossa ,
“Asynchronous Iterative Water-Filling for Gaussian
Frequency-Selective Interference Channels,” IEEE Trans. on
Information Theory, vol. 54, no. 7, July 2008.
Gesualdo Scutari, Daniel P. Palomar, and Sergio Barbarossa,
“Cognitive MIMO Radio: A Competitive Optimality Design Based on
Subspace Projections,” IEEE Signal Processing Magazine, Nov. 2008.
39. Signomial Programming:
Mung Chiang, “Geometric programming for
communication systems,” Foundations and Trends in Communications
and Information Theory , Now Publishers, vol. 2, no. 1-2,
Aug. 2006.
Mung Chiang,
Chee Wei Tan, Daniel P. Palomar,
Daniel O’Neill, and David Julian, “Power
Control by Geometric Programming,” IEEE Trans. on Wireless
Communications, vol. 6, no. 7, pp. 2640-2651, July
2007
40. Lasso
R. Tibshirani, “Regression shrinkage and selection via the
lasso,” Journal of the Royal Statistical Society. Series B
(Methodological) , pp. 267–288, 1996.
R. Tibshirani, M. Saunders, S. Rosset, J. Zhu, and K. Knight,
“Sparsity and smoothness via the fused lasso,” Journal of the Royal
Statistical Society: Series B (Statistical Methodology) , vol. 67,
no. 1, pp. 91–108, 2005.
M. Yuan and Y. Lin, “Model selection and estimation in
regression with grouped variables,” Journal of the Royal Statistical
Society: Series B (Statistical Methodology) , vol. 68, no. 1, pp.
49–67, 2006.
J. Friedman, T. Hastie, H. Höfling, and R. Tibshirani,
“Pathwise coordinate optimization,” The Annals of Applied Statistics
, vol. 1, no. 2, pp. 302–332, 2007
41. Index tracking in finance
Benidis, K., Feng, Y., and Palomar, D. P. (2017). Sparse
Portfolios for High-Dimensional Financial Index Tracking. IEEE
Transactions on Signal Processing
Xu, F., Xu, Z., and Xue, H., (2015). Sparse index tracking based
on L1/L2 model and algorithm. arXiv preprint
Jansen, R., and Van Dijk, R. (2002). Optimal benchmark tracking
with small portfolios The Journal of Portfolio Management vol. 28,
no. 2, pp. 33–39.
Beasley, J. E., Meade, N., and Chang, T.-J. (2003). An
evolutionary heuristic for the index tracking problem. European
Journal of Operational Research, vol. 148, no. 3, pp. 621–643.
Scozzari, A., Tardella, F., Paterlini, S., and, Krink, T. (2013).
Exact and heuristic approaches for the index tracking problem with
UCITS constraints. Annals of Operations Research, vol. 205, no. 1,
pp. 235–250.
42. Sensor Selection via Convex Optimization
Hero, Alfred O., and Douglas Cochran. "Sensor management:
Past, present, and future." IEEE Sensors Journal 11.12
(2011): 3064-3075.
Mo, Yilin, Roberto Ambrosino, and Bruno Sinopoli. "Sensor
selection strategies for state estimation in energy constrained
wireless sensor networks." Automatica 47.7 (2011):
1330-1338.
Joshi, Siddharth, and Stephen Boyd. "Sensor selection via
convex optimization." IEEE Transactions on Signal Processing
57.2 (2009): 451-462.
43. Multiobjective optimization and pareto optimality
Marler, R. Timothy, and Jasbir S. Arora. "Survey of
multi-objective optimization methods for engineering." Structural and
multidisciplinary optimization 26.6 (2004): 369-395.
Désidéri, Jean-Antoine. "Multiple-gradient descent algorithm
(MGDA) for multiobjective optimization." Comptes Rendus Mathematique
350.5-6 (2012): 313-318.
Fliege, Jörg, and Benar Fux
Svaiter. "Steepest descent methods for multicriteria optimization." Mathematical Methods of Operations Research
51.3 (2000): 479-494. Harada, Ken, Jun Sakuma, and Shigenobu
Kobayashi. "Local search for multiobjective function optimization:
pareto descent method." Proceedings of the 8th annual conference on
Genetic and evolutionary computation. ACM, 2006.
Fliege, Joerg, LM Grana Drummond, and Benar Fux Svaiter.
"Newton's method for multiobjective optimization." SIAM Journal on
Optimization 20.2 (2009): 602-626.
44. Saddle point methods for convex optimization
Zhu, Minghui, and Sonia Martínez. "On distributed convex
optimization under inequality and equality constraints." IEEE
Transactions on Automatic Control 57.1 (2012): 151-164.
Chang, Tsung-Hui, Angelia Nedić, and Anna Scaglione.
"Distributed constrained optimization by consensus-based primal-dual
perturbation method." IEEE Transactions on Automatic Control 59.6
(2014): 1524-1538.
45. Integer programming
Park, Jaehyun, and Stephen Boyd. "A semidefinite programming
method for integer convex quadratic minimization." Optimization
Letters (2017): 1-20.
Marchand, Hugues; Martin, Alexander; Weismantel, Robert;
Wolsey, Laurence (2002). "Cutting planes in integer and mixed
integer programming" (PDF). Discrete Applied Mathematics. 123:
387–446.
Castro,
J.; Nasini, S.; Saldanha-da-Gama, F. (2017). "A
cutting-plane approach for large-scale capacitated
multi-period facility location using a specialized
interior-point method"
Bader, David A.; Hart,
William E.; Phillips, Cynthia A.
(2004). "Parallel
Algorithm Design for Branch and Bound" (PDF). In Greenberg, H. J. Tutorials
on Emerging Methodologies and Applications in Operations
Research. Kluwer Academic Press.
46. Distributed Optimization
A. Nedic and
A. Ozdaglar. Distributed subgradient
methods for multi-agent optimization. IEEE Transactions on Automatic
Control , 54(1):48–61, Jan. 2009.
J. C. Duchi, A. Agarwal, and M. J. Wainwright. Dual
averaging for distributed optimization: Convergence analysis and
network
scaling. IEEE Transactions on Automatic Control , 57(3):592–606,
Mar. 2012
E. Wei and
A. Ozdaglar. Distributed
alternating direction method of
multipliers. In 51st IEEE Annual
Conference on
Decision and Control , pages 5445–5450, Dec. 2012.
J. F. C. Mota, J. M. F. Xavier, P. M. Q. Aguiar, and M.
Puschel. D-ADMM: A communication-efficient distributed algorithm for
separable optimization. IEEE Transactions on Signal Processing ,
61(10):2718–2723, May 2013.
W. Shi, Q.
Ling, K Yuan, G Wu, and W
Yin. On the linear convergence of the admm in
decentralized consensus opti-
mization. IEEE Transactions on Signal Processing , 62(7):1750–1761,
April 2014.
47. Cross-layer optimization in networks:
Lin, Xiaojun, Ness B. Shroff, and Rayadurgam Srikant. "A
tutorial on cross-layer optimization in wireless networks." IEEE
Journal on Selected areas in Communications 24.8 (2006):
1452-1463.
Chiang, Mung, et al. "Layering as optimization
decomposition: A mathematical theory of network architectures." Proceedings
of the IEEE 95.1 (2007): 255-312.
Georgiadis, Leonidas, Michael J. Neely, and Leandros
Tassiulas. "Resource allocation and cross-layer control in wireless
networks." Foundations and Trends® in Networking 1.1 (2006):
1-144.
Lin, Xiaojun, and Ness B. Shroff. "Joint rate control and
scheduling in multihop wireless networks." Decision and Control,
2004. CDC. 43rd IEEE Conference on. Vol. 2. IEEE, 2004.
48. Convex optimization in Control Theory
Linear Matrix Inequalities in System and Control Theory
Stephen Boyd, Laurent El Ghaoui, E. Feron, and V. Balakrishnan
Linear Controller Design – Limits of Performance, Stephen
Boyd and Craig Barratt
49. Convex optimization in Smart Grid
Samadi, Pedram, et al. "Optimal real-time pricing
algorithm based on utility maximization for smart grid." Smart Grid
Communications (SmartGridComm), 2010 First IEEE International
Conference on. IEEE, 2010.
Mohsenian-Rad, Amir-Hamed, et al. "Autonomous demand-side
management based on game-theoretic energy consumption scheduling for
the future smart grid." IEEE transactions on Smart Grid 1.3
(2010): 320-331.
Sortomme, Eric, et al. "Coordinated charging of plug-in
hybrid electric vehicles to minimize distribution system losses." IEEE
transactions on smart grid 2.1 (2011): 198-205.
50. Convex optimization in robotics
Verscheure, Diederik, et al. "Time-optimal path tracking for
robots: A convex optimization approach." IEEE Transactions on
Automatic Control 54.10 (2009): 2318-2327.
Schulman, John, et al. "Finding Locally Optimal,
Collision-Free Trajectories with Sequential Convex Optimization." Robotics:
science and systems. Vol. 9. No. 1. 2013.
Zhu, Minghui, and Sonia Martínez. "On distributed convex
optimization under inequality and equality constraints." IEEE
Transactions on Automatic Control 57.1 (2012): 151-164.
Derenick, Jason C., and John R. Spletzer. "Convex
optimization strategies for coordinating large-scale robot
formations." IEEE Transactions on Robotics 23.6 (2007): 1252-1259.