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Tight error bounds for log-determinant cones without constraint qualifications (2403.07295v2)
Published 12 Mar 2024 in math.OC, cs.NA, and math.NA
Abstract: In this paper, without requiring any constraint qualifications, we establish tight error bounds for the log-determinant cone, which is the closure of the hypograph of the perspective function of the log-determinant function. This error bound is obtained using the recently developed framework based on one-step facial residual functions.
- Linear convergence of a modified Frank-Wolfe algorithm for computing minimum-volume enclosing ellipsoids. Optimization Methods and Software, 23(1):5–19, 2008.
- C. L. Atwood. Optimal and efficient designs of experiments. Annals of Mathematical Statistics, 40:1570–1602, 1969.
- G. P. Barker and D. Carlson. Cones of diagonally dominant matrices. Pacific Journal of Mathematics, 57(1):15 – 32, 1975.
- Kernel-matrix determinant estimates from stopped Cholesky decomposition. Journal of Machine Learning Research, 24:71:1–71:57, 2023.
- On projection algorithms for solving convex feasibility problems. SIAM Review, 38(3):367–426, 1996.
- Strong conical hull intersection property, bounded linear regularity, Jameson’s property (G), and error bounds in convex optimization. Mathematical Programming, 86(1):135–160, Sep 1999.
- J. M. Borwein and H. Wolkowicz. Regularizing the abstract convex program. Journal of Mathematical Analysis and Applications, 83(2):495 – 530, 1981.
- S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, Cambridge, 2004.
- Solving natural conic formulations with Hypatia.jl. INFORMS Journal on Computing, 34:2686–2699, 2022.
- Conic optimization with spectral functions on Euclidean Jordan algebras. Mathematics of Operations Research, 48(4):1906–1933, 2023.
- Performance enhancements for a generic conic interior point algorithm. Mathematical Programming Computation, 15(1):53–101, 2023.
- First-order methods for sparse covariance selection. SIAM Journal on Matrix Analysis and Applications, 30(1):56–66, 2008.
- A. P. Dempster. Covariance selection. Biometrics, 28(1):157–175, 1972.
- J. Faraut and A. Korányi. Analysis on Symmetric Cones. Clarendon Press, Oxford, 1994.
- L. Faybusovich. Several Jordan-algebraic aspects of optimization. Optimization, 57(3):379–393, 2008.
- Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3):432–441, 2008.
- D. Henrion and J. Malick. Projection methods for conic feasibility problems: applications to polynomial sum-of-squares decompositions. Optimization Methods and Software, 26(1):23–46, 2011.
- A. J. Hoffman. On approximate solutions of systems of linear inequalities. Journal of Research of the National Bureau of Standards, 49(4):263–265, 1952.
- Matrix Analysis. Cambridge University Press, Cambridge, 1990.
- A. Kulesza and B. Taskar. Determinantal point processes for machine learning. Foundations and Trend® in Machine Learning, 5(2-3):123–286, 2012.
- Error bounds for convex inequality systems. In Generalized Convexity, Generalized Monotonicity: Recent Results, pages 75–110. Springer, US, 1998.
- Generalized power cones: optimal error bounds and automorphisms. ArXiv e-prints. To appear at the SIAM Journal on Optimization, 2023. arXiv:2211.16142.
- Error bounds, facial residual functions and applications to the exponential cone. Mathematical Programming, 200:229–278, 2023.
- Optimal error bounds in the absence of constraint qualifications with applications to the p𝑝pitalic_p-cones and beyond. arXiv preprint, 2021. arXiv:2109.11729.
- T. Liu and B. F. Lourenço. Convergence analysis under consistent error bounds. Foundations of Computational Mathematics, pages 1–51, 2022.
- B. F. Lourenço. Amenable cones: error bounds without constraint qualifications. Mathematical Programming, 186:1–48, 2021.
- Facial reduction and partial polyhedrality. SIAM Journal on Optimization, 28(3):2304–2326, 2018.
- Amenable cones are particularly nice. SIAM Journal on Optimization, 32(3):2347–2375, 2022.
- Z.-Q. Luo and P. Tseng. Error bounds and convergence analysis of feasible descent methods: a general approach. Annals of Operations Research, 46(1):157–178, 1993.
- MOSEK ApS. MOSEK Modeling Cookbook Release 3.3.0, 2022. URL: https://docs.mosek.com/modeling-cookbook/index.html.
- Y. Nesterov and A. Nemirovskii. Interior-point Polynomial Algorithms in Convex Programming. SIAM, Philadelphia, 1994.
- J.-S. Pang. Error bounds in mathematical programming. Mathematical Programming, 79(1):299–332, 1997.
- G. Pataki. Strong duality in conic linear programming: Facial reduction and extended duals. In Computational and Analytical Mathematics, volume 50, pages 613–634. Springer, New York, 2013.
- Gaussian Processes for Machine Learning. The MIT Press, Cambridge, MA, 2005.
- R. T. Rockafellar. Convex Analysis. Princeton University Press, New Jersey, 1997.
- H. Rue and L. Held. Gaussian Markov Random Fields: Theory and Applications. Chapman and Hall/CRC, New York, 2005.
- J. F. Sturm. Error bounds for linear matrix inequalities. SIAM Journal on Optimization, 10(4):1228–1248, 2000.
- M. J. Todd. Minimum-volume Ellipsoids: Theory and Algorithms. SIAM, Philadelphia, 2016.
- S. Van Aelst and P. Rousseeuw. Minimum volume ellipsoid. Wiley Interdisciplinary Reviews: Computational Statistics, 1(1):71–82, 2009.
- H. Waki and M. Muramatsu. Facial reduction algorithms for conic optimization problems. Journal of Optimization Theory and Applications, 158(1):188–215, 2013.
- Fused multiple graphical lasso. SIAM Journal on Optimization, 25(2):916–943, 2015.
- An efficient linearly convergent regularized proximal point algorithm for fused multiple graphical Lasso problems. SIAM Journal on Mathematics of Data Science, 3(2):524–543, 2021.
- Z. Zhou and A. M.-C. So. A unified approach to error bounds for structured convex optimization problems. Mathematical Programming, 165(2):689–728, Oct 2017.
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