Books
- Introduction to Derivative Free Optimization with A. R. Conn and L. N. Vicente. Available from SIAM Series on Mathematical Programming. December 2008 / approx. xii + 277 pages / Softcover / ISBN: 978-0-898716-68-0. The errata is available here
Papers
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Linear interpolation gives better gradients than Gaussian smoothing in derivative-free optimization, with Albert S Berahas, Liyuan Cao, and Krzysztof Choromanski, 2019.
- A Theoretical and Empirical Comparison of Gradient Approximations in Derivative-Free Optimization, with Albert S Berahas, Liyuan Cao, and Krzysztof Choromanski, 2019.
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Novel and Efficient Approximations for Zero-One Loss of Linear Classifiers with Hiva Ghanbari, and Minhan Li, 2019.
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A Stochastic Line Search Method with Convergence Rate Analysis, with Courtney Paquette, 2018.
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Inexact SARAH Algorithm for Stochastic Optimization, with Lam M. Nguyen and Martin Takáč, 2018.
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Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning, with Frank E. Curtis, Informs TutORials, pages 89–114, 2017.
- Stochastic Recursive Gradient Algorithm for Nonconvex Optimization, with L. Nguyen, Jie Liu and M. Takac, Technical Report, 2017.
- SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient, with L. Nguyen, Jie Liu and M. Takac, to appear in ICML 2017.
- Optimal Generalized Decision Trees via Integer Programming, with O. Gunluk, M. Menickelly and J. Kalagnanam, Technical Report, 2017.
- Black-box Optimization in Machine Learning with Trust-Region Based Derivative Free Algorithms, with H. Ghanbari, Technical Report, 2017.
- Proximal Quasi-Newton Methods for Convex Optimization, with H. Ghanbari, submitted, 2016
- Convergence Rate Analysis of a Stochastic Trust Region Method via Submartingales with Jose Blanchet, Coralia Cartis and Matt Menickelly, 2018.
- Stochastic Optimization Using a Trust-Region Method and Random Models, with R. Chen and M. Menickelly, Mathematical Programming, 2018.
- Global convergence rate analysis of unconstrained optimization methods based on probabilistic models, with C. Cartis, Mathematical Programming, 2018.
- Alternating direction methods for non convex optimization with applications to second-order least-squares and risk parity portfolio selection, with Xi Bai, 2015, technical report.
- Practical Inexact Proximal Quasi-Newton Method with Global Complexity Analysis, with Xiaocheng Tang, Mathematical Programming, 2016, 160(1-2) pp 495–529
- Least-squares approach to risk parity in portfolio selection, with X. Bai and R. Tutuncu, Quantitative Finance, 2016, 16(3), pp 357-376.
- Aligning ligand binding cavities by optimizing superposed volume, with B. Chen and R. Chen, in BIBM 2012.
- Convergence of trust-region methods based on probabilistic models, with A. Bandeira and L.N. Vicente, SIOPT, 14(3), (2014), pp. 1238-1264.
- Fast first-order methods for composite convex optimization with backtracking, with D. Goldfarb, FOCM, 2014, 14: 389-417.
- Efficient Block-coordinate Descent Algorithms for the Group Lasso. with Z. Qin, and D. Goldfarb. Math Prog. Comp., 2013, Volume 5, Issue 2, pp 143-169.
- Computation of sparse low degree interpolating polynomials and their application to derivative-free optimization, with A. Bandeira and L.N. Vicente, Math. Prog., Series B, (2012), 134, pp 223-257.
- On partially sparse recovery, with A. Bandeira and L.N. Vicente, 2011
- Fast alternating linearization methods for minimizing the sum of two convex functions, with D. Goldfarb and S. Ma, 2013, Math. Prog. Series A, 141: pp 349-382.
- Sparse Inverse Covariance Selection via Alternating Linearization Methods, with D. Goldfarb and S. Ma, NIPS 2010
- SINCO – a greedy coordinate ascent method for sparse inverse covariance selection problem, with I Rish, 2009
- Optimization Methods for Sparse Inverse Covariance Selection Problem. with S. Ma, In S. Sra, S. Nowozin, and S. J. Wright editors: Optimization for Machine Learning, MIT Press, 2010
- Row by row method for semidefinite programming, with Z. Wen, D. Goldfarb and S. Ma, submitted, 2009
- A Derivative-Free Algorithm for the Least-square minimization, with H. Zhang and A.R. Conn, submitted, 2009
- Self-correcting geometry in model-based algorithms for derivative-free unconstrained optimization with Ph. L. Toint, to appear, 2009.
- A MAP approach to learning sparse gaussian markov networks. with N. Bani Asadi, I. Rish, D. Kanevsky, B. Ramabhadran, ICCASP 2009.
- Global Convergence of General Derivative-Free Trust-Region Algorithms to First and Second Order Critical Points , with A.R. Conn and L.N. Vicente, SIAM J. on Optimization, (2009).
- Geometry of Sample Sets in Derivative Free Optimization: polynomial regression and incomplete interpolation., with A.R. Conn and L.N. Vicente, IMA Journal of Numerical Analysis 2008 28(4):721-748.
- Geometry of Interpolation Sets in Derivative Free Optimization., with A.R. Conn and L.N. Vicente, Mathematical Programming, 111 (2008), 141-172.
- Product-Form LDL^T Factorizations in Interior-Point Methods for Convex Quadratic , with D. Goldfarb, IMA Journal of Numerical Analysis 2008 28(4):806-826.
- IBM Research TRECVID-2006 Video Retrieval System with M. Campbell, S. Ebadollahi, D. Joshi, M. Naphade, A. Natsev, J. Seidl, J. R. Smith, J. Tesic and L. Xie.
- Detecting Generic Visual Events with Temporal Cues. with Lexing Xie, Dong Xu, Shahram Ebadollahi, Shih-Fu Chang, John R. Smith. In Proc. 40th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, October 2006.
- An Efficient Implementation of an Active Set Method for SVM, J. of Machine Learning Research 7 (2006) 2237-2257.
- (Conference version: Incas: An incremental active set method for SVM, with Shai Fine (2002) ).
- Product-form Cholesky factorization in interior point methods for second-order cone programming, with D. Goldfarb, Mathematical Programming, v. 103 (2005), pp. 153-179.
- A product-form Cholesky factorization method for handling dense columns in interior point methods for linear programming, with D. Goldfarb, Mathematical Programming, v. 99 (2004), pp. 1-34.
- Incremental learning and selective sampling via parametric optimization framework for SVM, with S. Fine, in “Advances in Neural Information Processing Systems” 14, MIT Press, (2002) 705-711.
- Efficient SVM training using low-rank Kernel representations, with S. Fine, J. of Machine Learning Research, Special issue on Kernel methods, 2(2001) 243-264.
- Manual for FORTRAN software package DFO v1.2, (2001).
- Parametric linear semidefinite programming, , In “Handbook on Semidefinite Programming”, eds. H. Wolkowicz, R. Saigal, L. Vandenberghe, pp 92–110. Kluwer, 2000.
- On parametric semidefinite programming, with D. Goldfarb, Applied Numerical Mathematics, v. 29(3), pp. 361-377, 1999.
- Modified barrier-penalty functions for constrained minimization problems, with Goldfarb D., Polyak R. and Yusefovich B. Computational Optimization and Applications, v. 14(1), pp. 55-74, 1999.
- A derivative free optimization algorithm in practice, with Conn A. R. and Toint Ph. L., Proceedings of 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, St. Louis, MO, 1998.
- Interior point trajectories in semidefinite programming, with D. Goldfarb, SIAM J. on Optimization v. 8(4), pp. 871-886, 1998.
- Recent progress in unconstrained nonlinear optimization without derivatives, with A.R. Conn and Ph. L. Toint, Mathematical Programming v. 79 (1997) 397-414.
- On the convergence of derivative-free methods for unconstrained optimization, with A.R. Conn and Ph. L. Toint, “Approximation Theory and Optimization: Tributes to M. J. D. Powell”, eds. A. Iserles and M. Buhmann, pp 83–108, 1997.
- Issues related to interior point methods for linear and semidefinite programming., PhD Thesis, Dept. of IEOR, Columbia University, 1997.
- Extension of Karmarkar’s algorithm to convex quadratically constrained quadratic , with A. Nemirovskii, Mathematical Programming, v. 72 (1996) 273-289.