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Contact Info

William H. Sanders
Interim Department Head
ECE ILLINOIS
1406 W. Green St.
Urbana, IL 61801-2918
Ph: (217) 333-2300
Fax: (217) 244-7075
whs@illinois.edu

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Angelia Nedich

Electrical and Computer Engineering
Angelia  Nedich
Angelia Nedich
Assistant Professor
  • Industrial and Enterprise Systems Engineering
201 D Transportation Building
104 S. Mathews
Urbana Illinois 61801

Affiliation

  • Industrial and Enterprise Systems Engineering

Education

  • B.S. Mathematics, University of Montenegro, Yugoslavia, 1987
  • M.S. Mathematics, University of Belgrade, Yugoslavia, 1991
  • Ph.D. Mathematics and Mathematical Physics, Moscow State University, 1994
  • Ph.D. Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 2002

For more information

Research Interests

  • Communication networks and sensor networks
  • Optimization applications in wireless
  • Duality theory
  • Stochastic approximation algorithms
  • Variational inequalities
  • Distributed optimization
  • Convex and nonconvex optimization

Books Authored or Co-Authored (Original Editions)

Bertsekas D., Nedic A., and Ozdaglar A. Convex Analysis and Optimization, Athena Scientific, Belmont, Massachusetts, 2003.

Chapters in Books

  • D.P. Bertsekas, V.S. Borkar, and A. Nedic, ”Improved Temporal Difference Methods with Linear Function Approximation,” in Learning and Approximate Dynamic Programming by J. Si, A. G. Barto, W. B. Powell, and D. Wunsch II (Eds.), IEEE Press, pp. 235-260, 2004.
  • S. Sundhar Ram, V.V. Veeravalli, and A. Nedic, “Distributed and Recursive Nonlinear Least Square Parameter Estimation: Linear and Separable Models,” in the book Sensor Networks: Where Theory Meets Practice, edited by G. Ferrari, Springer-Verlag, pp. 17-38, 2009.
  • A. Nedic and A. Ozdaglar, “Cooperative Distributed Multi-Agent Optimization,” in the book Convex Optimization in Signal Processing and Communications, edited by Y. Eldar and D. Palomar, Cambridge University Press, pp. 340-386, 2010.
  • K. Srivastava, A. Nedic and D. Stipanovic "Distributed Bregman-Distance Algorithms for Min-Max Optimization," a book chapter to appear in the book “Agent-Based Optimization” edited by I. Czarnowski, P. Jedrzejowicz and J. Kacprzyk, series of Springer Studies in Computational Intelligence (SCI), pp. 143-174, 2013.

Selected Articles in Journals

  • A. Nedic, "The Three-Step Gradient Pro jection Method for Minimization Problems,” (Russian) Izv. Vyssh. Uchebn. Zaved. Mat., No. 10, 1993, 32–37; translation in Russian Math. (Iz. VUZ), 37, No. 10, 1993, pp. 30–36.
  • F.P. Vasil’ev and A. Nedic, “A Three-Step Regularized Gradient-Projection Method for Solving Minimization Problems with Inexact Initial Data,” (Russian) Izv. Vyssh. Uchebn. Zaved. Mat., 1993, No. 12, 35–43; translation in Russian Math. (Iz. VUZ), 37, No. 12, pp. 34–43.
  • F.P. Vasil’ev and A. Nedic, “A Regularized Continuous Gradient-Projection Method of the Second Order,” (Russian) Vestnik Moskov. Univ. Ser. XV Vychisl. Mat. Kibernet., No. 2, 1994, 3–11; translation in Moscow Univ. Comput. Math. Cybernet., No. 2, 1994, pp. 1–9.
  • A. Nedic, “The Regularized Continuous Gradient-Projection Method for Minimization Problems with Inexact Initial Data,” (Russian) Vestnik Moskov. Univ. Ser. XV Vychisl. Mat. Kibernet., No. 1, 1994, 3–10; translation in Moscow Univ. Comput. Math. Cybernet., No. 1, 1994, pp. 1–7.
  • F.P. Vasil’ev and A. Nedic, “A Version of the Regularized Gradient Projection Method,” (Russian) Zh. Vychisl. Mat. i Mat. Fiz., 34, No. 4, 1994, 511–519; translation in Comput. Math. Math. Phys., 34, No. 4, 1994, pp. 431–439.
  • A. Nedic, “ A Third-Order Continuous Gradient-Projection Method for Minimization Problems,” (Russian) Differ. Urav. 30, No. 11, 1994, 1914–1922; translation in Differential Equations No. 11, 1994, pp. 1767–1774.
  • F.P. Vasil’ev and A. Nedic, “A Regularized Third-Order Continuous Gradient Projection Method,” (Russian) Differ. Urav. 30, No. 12, 1994, 2033–2042; translation in Differential Equations No. 12, 1994, pp. 1869–1877.
  • A.S. Antipin, A. Nedic, and M. Yachimovich, “A Three-Step Linearization Method for Minimization Problems,” (Russian) Izv. Vyssh. Uchebn. Zaved. Mat., No. 1994, 12, 3–7; translation in Russian Math. (Iz. VUZ), 38, No. 12, 1994, pp. 1–5.
  • F.P. Vasil’ev, A. Nedic, and M. Yachimovich, “A Three-Step Regularized Linearization Method for solving minimization problems,” (Russian) Izv. Vyssh. Uchebn. Zaved. Mat., No. 12, 1994, 25–32; translation in Russian Math. (Iz. VUZ), 38, No. 12, 1994, pp. 23–30.
  • F.P. Vasil’ev, T.V. Amochkina, and A. Nedic, “On a Regularized Variant of the Second-Order Continuous Gradient Projection Method,” (Russian) Vestnik Moskov. Univ. Ser. XV Vychisl. Mat. Kibernet., No. 3, 1995, 39–46; translation in Moscow Univ. Comput. Math. Cybernet., No. 3, 1995, pp. 33–39.
  • F.P. Vasil’yev, A. Nedic, and O. Obradovic, “The Continuous Version of the Proximal Point Method for the Minimization Problem with Inexactly Defined Initial Data,” Math. Montisnigri, 5, 1995, pp. 123–139.
  • F.P. Vasiljev, and A. Nedic, “A Regularized Continuous Projection Gradient Method of the Fourth Order,” Yugosl. J. Oper. Res., 5, No. 2, 1995, pp. 195–209.
  • T.V. Amochkina and A. Nedic, “On a Variant of the Second-Order Continuous Gradient Pro jection Method and its Discrete Analogue,” (Russian) Vestnik Moskov. Univ. Ser. XV Vychisl. Mat. Kibernet., No.2, 1995, 5–11; translation in Moscow Univ. Comput. Math. Cybernet., No. 2, 1995, pp. 1–7.
  • A. Nedic, “The Continuous Projection-Gradient Method of the Fourth Order,” Yugosl. J. Oper. Res., 5, No. 1, 1995, pp. 27–38.
  • A.S. Antipin, A. Nedic, and M. Yachimovich, “A Four-Step Linearization Method for Minimization Problems,” (Russian) Math. Montisnigri, 4, 1995, pp. 1–11.
  • A. Nedic, “A Four-Step Gradient Projection Method for Minimization Problems,” (Russian) Math. Montisnigri, 4, 1995, pp. 55–64.
  • F.P. Vasil’ev and A. Nedic, “A Four-Step Regularized Gradient Projection Method for Solving Minimization Problems with Inexact Initial Data,” (Russian) Math. Montisnigri, 4, 1995, pp. 83–101.
  • A. Nedic and M. Yachimovich, “A Third-Order Continuous Linearization Method for Solving Convex Programming Problems,” (Russian) Differential Equations, 31, No. 9, 1995, pp. 1437–1441.
  • F.P. Vasil’ev, A. Nedic, and M. Yachimovich, “A Third-Order Regularized Continuous Method of Linearization,” (Russian) Differ. Uravn. 31, No. 10, 1995, 1622–1627; translation in Differential Equations, No. 10, 1995, pp. 1582–1588.
  • F.P. Vasil’ev, T.V. Amochkina, and A. Nedic, “On a Regularized Version of the Two-Step Gradient Pro jection Method,” (Russian) Vestnik Moskov. Univ. Ser. XV Vychisl. Mat. Kibernet., No. 1, 1996, 35–42; translation in Moscow Univ. Comput. Math. Cybernet., No. 1, 1996, pp. 31–37.
  • F.P. Vasil’ev, A. Nedic, and M. Yachimovich, “A Two-Step Regularized Linearization Method for Solving Minimization Problems., (Russian) Zh. Vychisl. Mat. i Mat. Fiz., 36, No. 5, 1996, 9–19; translation in Comput. Math. Math. Phys., 36, No. 5, 1996, pp. 559–567.
  • F.P. Vasil’ev, A. Nedic, and M. Yachimovich, “A Four-Step Regularized Linearization Method for Solving Minimization Problems,” Math. Montisnigri, 6, 1996, pp. 109–125.
  • F.P. Vasil’ev, A. Nedic, and M. Yachimovich, “A Regularized Continuous Linearization Method for Minimization Problems with Inexact Initial Data,” (Russian) Zh. Vychisl. Mat. i Mat. Fiz., 36, No. 3, 1996, 35–43; translation in Comput. Math. Math. Phys., 36, No. 3, 1996, pp. 309–316.
  • A.S. Antipin, A. Nedic, and M. Yachimovich, “A Two-Step Linearization Method for Minimization Problems,(Russian) Zh. Vychisl. Mat. i Mat. Fiz., 36, No. 4, 1996, 18–25; translation in Comput. Math. Math. Phys., 36, No. 4, 1996, pp. 431–437.
  • F.P. Vasil’ev, A. Nedic, and M. Yachimovich, “A Second-Order Regularized Continuous Linearization Method for Minimization Problems with Inexact Initial Data,” (Russian) Vestnik Moskov. Univ. Ser. XV Vychisl. Mat. Kibernet., No. 3, 1996, 5–12, 81; translation in Moscow Univ. Comput. Math. Cybernet., No. 3, 1996, pp. 1–7.
  • A. Nedic, “An Optimal Control Problem on a Half-line,” Math. Montisnigri, 6, 1996, pp. 43–55.
  • A.S. Antipin, and A. Nedic, “A Second-Order Continuous Linearization Method for Convex Programming Problems,” (Russian) Vestnik Moskov. Univ., Ser. XV Vychisl. Mat. Kibernet., No. 2, 1996, 3–12; translation in Moscow Univ. Comput. Math. Cybernet., No. 2, 1996, pp. 1–9.
  • F.P. Vasil’ev, A. Nedic, and O. Obradovich, “Continuous Regularized Proximal Minimization Method,” Numerical Methods in Mathematical Physics, Comput. Math. Model., 8, No. 2, 1997, pp. 85–94.
  • A. Nedic, M. Jacimovic, and A.S. Antipin, “The Continuous Linearization Method of the Fourth Order,” Yugosl. J. Oper. Res., 7, No.. 1, 1997, pp. 39–47.
  • F.P. Vasiljev, A. Nedic, and M. Jacimovic, “A Regularized Continuous Linearization Method of the Fourth Order,” Yugosl. J. Oper. Res., 7, No. 2, 1997, pp. 217–229.
  • A. Nedic and D.P. Bertsekas, “Incremental Subgradient Methods for Nondifferentiable Optimization,” SIAM J. on Optimization, 12, No. 1, 2001, pp. 109–138.
  • A. Nedic, D.P. Bertsekas, and V.S. Borkar, “Distributed asynchronous incremental subgradient methods,” Inherently parallel algorithms in feasibility and optimization and their applications (Haifa, 2000), 381–407, Stud. Comput. Math., 8, North-Holland, Amsterdam, 2001.
  • A. Nedic and D.P. Bertsekas, “Convergence rate of incremental subgradient algorithms,” Stochastic optimization: algorithms and applications (Gainesville, FL, 2000), 223–264, Appl. Optim., 54, Kluwer Acad. Publ., Dordrecht, 2001.
  • A. Nedic and D.P. Bertsekas, ”Least-Squares Policy Evaluation Algorithms with Linear Function Approximation,” Journal of Discrete Event Systems, Vol. 13, pp. 79-110 , 2003
  • A. Nedic, A. Ozdaglar, and A. Rubinov, “Abstract Convexity for Nonconvex Optimization Duality,” Optimization, vol. 56, 655--674, 2007.
  • A. Nedic and A. Ozdaglar, “A Geometric Framework for Nonconvex Optimization Duality using Augmenting Lagrangian Functions,” Journal of Global Optimization, 40 (4) 545--573, 2008.
  • A. Nedic and A. Ozdaglar, “Separation of Nonconvex Sets with General Augmenting Functions,” Mathematics of Operations Research, 33 (3), 587–605, 2008.
  • A. Nedic and A. Ozdaglar, “Approximate Primal Solutions and Rate Analysis in Dual Subgradient Methods,” SIAM Journal on Optimization 19 (4), 1757-1780, 2009.
  • A. Nedic and A. Ozdaglar, “Distributed Subgradient Methods for Multiagent Optimization,” IEEE Transactions on Automatic Control 54 (1) 48-61, 2009.
  • A. Nedic and A. Ozdaglar, “Subgradient Methods for Saddle-Point Problems,” Journal of Optimization Theory and Applications, 142 (1) 205-208, 2009.
  • A. Nedic and D.P. Bertsekas, “The Effect of Deterministic Noise in Subgradient Methods,” Mathematical Programming 125 (1) 75-99, 2010
  • A. Nedic and A. Ozdaglar, "Convergence Rate for Consensus with Delays Journal of Global Optimization," 47 (3) 437--456, 2010.
  • A. Nedic, A. Olshevsky, A. Ozdaglar, and J.N. Tsitsiklis, “On Distributed Averaging Algorithms and Quantization Effects” IEEE Transactions on Automatic Control, 54 (11) 2506-2517, 2009. A short version is also in Proceedings of the 47th CDC Conference 2008.
  • A. Nedic, A. Ozdaglar, and A.P. Parrilo, “Constrained Consensus and Optimization in Multi-Agent Networks," IEEE Transactions on Automatic Control 55 (4) 922-938, 2010.
  • S. Sundhar Ram, V.V. Veeravalli, and A. Nedic, “Distributed and Recursive Parameter Estimation in Parametrized Linear State-Space Models,” IEEE Transactions on Automatic Control, 55 (2) 488-492, 2010.
  • S. Sundhar Ram, A. Nedic, and V.V. Veeravalli, “Incremental Stochastic Subgradient Algorithms for Convex Optimization,” SIAM Journal on Optimization 20 (2) 691-717, 2009.
  • S.S. Ram, A. Nedic, and V.V. Veeravalli, “Distributed Stochastic Subgradient Projection Algorithms for Convex Optimization,” Journal of Optimization Theory and Applications, 147 (3) 516–545, 2010
  • S.S. Ram, A. Nedic, and V.V. Veeravalli, "A New Class of Distributed Optimization Algorithms: Application to Regression of Distributed Data,” Optimization Methods and Software," Optimization Methods and Software, 27 (1) 71-88, 2012.
  • J. Koshal, A. Nedic and U.V. Shanbhag, "Multiuser Optimization: Distributed Algorithms and Error Analysis," SIAM Journal on Optimization 21 (3) 1046-1081, 2011.
  • B. Touri and A. Nedic, “On Ergodicity, Infinite Flow and Consensus in Random Models,” IEEE Transactions on Automatic Control 56 (7) 1593-1605, 2011.
  • F. Yousefian, A. Nedic and U. V. Shanbhag, “On Stochastic Gradient and Subgradient Methods with Adaptive Steplength Sequences,” (a short version) in Automatica 48 (1) 56-67, 2012. Longer version on arxiv at http://arxiv.org/abs/1105.4549
  • A. Nedic, “Asynchronous Broadcast-Based Convex Optimization over a Network,” IEEE Transactions on Automatic Control 56 (6) 1337-1351, 2011.
  • A. Nedic, “Random Projection Algorithms for Convex Minimization Problems,” Mathematical Programming, Series B, Special issue in honor of Paul Tseng on Large Scale Optimization: Analysis, Algorithms and Applications, 129, 225-253, 2011
  • K. Srivastava and A. Nedic, “Distributed Asynchronous Constrained Stochastic Optimization,” IEEE Journal of Selected Topics in Signal Processing, Special issue on “Gossiping Algorithms Design and Applications,” edited by M. Coates, M. Gastpar, A. Scaglione, J. Tsitsiklis, and M. Vetterli, 5 (4) 772-790, 2011.
  • B. Touri and A. Nedic, “On Backward Product of Stochastic Matrices,” Automatica 48 (8) 1477-1488, 2012.
  • B. Touri and A. Nedic, "On Approximations and Ergodicity Classes in Random Chains," IEEE Transactions on Automatic Control 57 (11) 2718-2730, 2012.
  • J. Koshal, A. Nedic and U.V. Shanbhag, "Regularized Iterative Stochastic Approximation Methods for Variational Inequality Problems," IEEE Transactions on Automatic Control, 58 (3) 594-609, 2013.

Research Honors

NSF CAREER Award 2007 in Operations Research