Abstract
We give a characterization of the Hoffman constant of a system of linear constraints in \({{\mathbb {R}}}^n\) relative to a reference polyhedron \(R\subseteq {{\mathbb {R}}}^n\). The reference polyhedron R represents constraints that are easy to satisfy such as box constraints. In the special case \(R = {{\mathbb {R}}}^n\), we obtain a novel characterization of the classical Hoffman constant. More precisely, suppose \(R\subseteq \mathbb {R}^n\) is a reference polyhedron, \(A\in {{\mathbb {R}}}^{m\times n},\) and \(A(R):=\{Ax: x\in R\}\). We characterize the sharpest constant \(H(A\vert R)\) such that for all \(b \in A(R) + {{\mathbb {R}}}^m_+\) and \(u\in R\)
where \(P_A(b) = \{x\in {{\mathbb {R}}}^n:Ax\le b\}\). Our characterization is stated in terms of the largest of a canonical collection of easily computable Hoffman constants. Our characterization in turn suggests new algorithmic procedures to compute Hoffman constants.
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References
Amelunxen, D., Bürgisser, P.: A coordinate-free condition number for convex programming. SIAM J. Optim. 22(3), 1029–1041 (2012)
Azé, D., Corvellec, J.: On the sensitivity analysis of Hoffman constants for systems of linear inequalities. SIAM J. Optim. 12(4), 913–927 (2002)
Bauschke, H., Bolte, J., Teboulle, M.: A descent lemma beyond Lipschitz gradient continuity: first-order methods revisited and applications. Math. Oper. Res. 42(2), 330–348 (2016)
Beck, A., Shtern, S.: Linearly convergent away-step conditional gradient for non-strongly convex functions. Math. Program. 164, 1–27 (2017)
Bürgisser, P., Cucker, F.: Condition. Springer, Berlin (2013)
Burke, J., Tseng, P.: A unified analysis of Hoffman’s bound via Fenchel duality. SIAM J. Optim. 6(2), 265–282 (1996)
Epelman, M., Freund, R.: A new condition measure, preconditioners, and relations between different measures of conditioning for conic linear systems. SIAM J. Optim. 12, 627–655 (2002)
Freund, R.: Complexity of convex optimization using geometry-based measures and a reference point. Math. Program. 99, 197–221 (2004)
Freund, R., Vera, J.: Some characterizations and properties of the “distance to ill-posedness” and the condition measure of a conic linear system. Math. Program. 86, 225–260 (1999)
Freund, R., Vera, J.: On the complexity of computing estimates of condition measures of a conic linear system. Math. Oper. Res. 28(4), 625–648 (2003)
Garber, D.: Fast rates for online gradient descent without strong convexity via Hoffman’s bound. arXiv preprint arXiv:1802.04623 (2018)
Granot, F., Skorin-Kapov, J.: Some proximity and sensitivity results in quadratic integer programming. Math. Program. 47(1–3), 259–268 (1990)
Güler, O., Hoffman, A., Rothblum, U.: Approximations to solutions to systems of linear inequalities. SIAM J. Matrix Anal. Appl. 16(2), 688–696 (1995)
Gutman, D., Peña, J.: The condition number of a function relative to a set. arXiv preprint arXiv:1901.08359 (2019)
Hoffman, A.: On approximate solutions of systems of linear inequalities. J. Res. Natl. Bur. Stand. 49(4), 263–265 (1952)
IBM ILOG CPLEX Optimization Studio-CPLEX. Users manual-version 12 release 6 (2013)
Jourani, A.: Hoffman’s error bound, local controllability, and sensitivity analysis. SIAM J. Control Optim. 38(3), 947–970 (2000)
Júdice, J.: Algorithms for linear programming with linear complementarity constraints. Top 20(1), 4–25 (2012)
Klatte, D., Thiere, G.: Error bounds for solutions of linear equations and inequalities. Z. Oper. Res. 41(2), 191–214 (1995)
Lacoste-Julien, S., Jaggi, M.: On the global linear convergence of Frank–Wolfe optimization variants. In: Advances in Neural Information Processing Systems (NIPS) (2015)
Leventhal, D., Lewis, A.: Randomized methods for linear constraints: convergence rates and conditioning. Math. Oper. Res. 35, 641–654 (2010)
Lewis, A.: Ill-conditioned convex processes and linear inequalities. Math. Oper. Res. 24, 829–834 (1999)
Lewis, A.: The structured distance to ill-posedness for conic systems. Math. Oper. Res. 29, 776–785 (2005)
Li, W.: The sharp Lipschitz constants for feasible and optimal solutions of a perturbed linear program. Linear Algebra Appl. 187, 15–40 (1993)
Lu, H.: Relative-continuity” for non-Lipschitz non-smooth convex optimization using stochastic (or deterministic) mirror descent. arXiv preprint arXiv:1710.04718 (2017)
Lu, H., Freund, R., Nesterov, Y.: Relatively smooth convex optimization by first-order methods, and applications. SIAM J. Optim. 28(1), 333–354 (2018)
Luo, Z., Tseng, P.: Error bounds and convergence analysis of feasible descent methods: a general approach. Ann. Oper. Res. 46(1), 157–178 (1993)
Mangasarian, O., Shiau, T.-H.: Lipschitz continuity of solutions of linear inequalities, programs and complementarity problems. SIAM J. Control Optim. 25(3), 583–595 (1987)
Necoara, I., Nesterov, Y., Glineur, F.: Linear convergence of first order methods for non-strongly convex optimization. Math. Program. 175, 69–107 (2019)
Nguyen, T.: A stroll in the jungle of error bounds. arXiv preprint arXiv:1704.06938 (2017)
Pang, J.S.: Error bounds in mathematical programming. Math. Program. 79, 299–332 (1997)
Peña, J.: Understanding the geometry on infeasible perturbations of a conic linear system. SIAM J. Optim. 10, 534–550 (2000)
Peña, J.: A characterization of the distance to infeasibility under block-structured perturbations. Linear Algebra Appl. 370, 193–216 (2003)
Peña, J., Rodríguez, D.: Polytope conditioning and linear convergence of the Frank–Wolfe algorithm. Math. Oper. Res. 44, 1–18 (2019)
Pineda, S., Bylling, H., Morales, J.: Efficiently solving linear bilevel programming problems using off-the-shelf optimization software. Optim. Eng. 19(1), 187–211 (2018)
Ramdas, A., Peña, J.: Towards a deeper geometric, analytic and algorithmic understanding of margins. Optim. Methods Softw. 31(2), 377–391 (2016)
Renegar, J.: Incorporating condition measures into the complexity theory of linear programming. SIAM J. Optim. 5, 506–524 (1995)
Renegar, J.: Linear programming, complexity theory and elementary functional analysis. Math. Program. 70, 279–351 (1995)
Robinson, S.: Bounds for error in the solution set of a perturbed linear program. Linear Algebra Appl. 6, 69–81 (1973)
Siddiqui, S., Gabriel, S.: An SOS1-based approach for solving mpecs with a natural gas market application. Netw. Spat. Econ. 13(2), 205–227 (2013)
Stein, O.: Error bounds for mixed integer linear optimization problems. Math. Program. 156(1–2), 101–123 (2016)
Stewart, G.: On scaled projections and pseudoinverses. Linear Algebra Appl. 112, 189–193 (1989)
Teboulle, M.: A simplified view of first order methods for optimization. Math. Program. 1, 1–30 (2018)
Todd, M.: A Dantzig–Wolfe-like variant of Karmarkar’s interior-point linear programming algorithm. Oper. Res. 38(6), 1006–1018 (1990)
Van Ngai, H., Théra, M.: Error bounds for systems of lower semicontinuous functions in Asplund spaces. Math. Program. 116(1–2), 397–427 (2009)
Vavasis, S., Ye, Y.: A primal-dual interior point method whose running time depends only on the constraint matrix. Math. Program. 74(1), 79–120 (1996)
Wang, P., Lin, C.: Iteration complexity of feasible descent methods for convex optimization. J. Mach. Learn. Res. 15(1), 1523–1548 (2014)
Xia, W., Vera, J., Zuluaga, L.F.: Globally solving non-convex quadratic programs via linear integer programming techniques. INFORMS J. Comput. (2020). https://doi.org/10.1287/ijoc.2018.0883
Zalinescu, C.: Sharp estimates for Hoffman’s constant for systems of linear inequalities and equalities. SIAM J. Optim. 14(2), 517–533 (2003)
Zhang, S.: Global error bounds for convex conic problems. SIAM J. Optim. 10(3), 836–851 (2000)
Zhou, Z., So, A.: A unified approach to error bounds for structured convex optimization problems. Math. Program. 165(2), 689–728 (2017)
Acknowledgements
Javier Peña’s research has been funded by NSF Grant CMMI-1534850.
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Peña, J., Vera, J.C. & Zuluaga, L.F. New characterizations of Hoffman constants for systems of linear constraints. Math. Program. 187, 79–109 (2021). https://doi.org/10.1007/s10107-020-01473-6
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DOI: https://doi.org/10.1007/s10107-020-01473-6