TY - GEN
T1 - No self-concordant barrier interior point method is strongly polynomial
AU - Allamigeon, Xavier
AU - Gaubert, Stéphane
AU - Vandame, Nicolas
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/9/6
Y1 - 2022/9/6
N2 - It is an open question to determine if the theory of self-concordant barriers can provide an interior point method with strongly polynomial complexity in linear programming. In the special case of the logarithmic barrier, it was shown in [Allamigeon, Benchimol, Gaubert and Joswig, SIAM J. on Applied Algebra and Geometry, 2018] that the answer is negative. In this paper, we show that none of the self-concordant barrier interior point methods is strongly polynomial. This result is obtained by establishing that, on parametric families of convex optimization problems, the log-limit of the central path degenerates to a piecewise linear curve, independently of the choice of the barrier function. We provide an explicit linear program that falls in the same class as the Klee-Minty counterexample for the simplex method, i.e., in which the feasible region is a combinatorial cube and the number of iterations is ω(2n).
AB - It is an open question to determine if the theory of self-concordant barriers can provide an interior point method with strongly polynomial complexity in linear programming. In the special case of the logarithmic barrier, it was shown in [Allamigeon, Benchimol, Gaubert and Joswig, SIAM J. on Applied Algebra and Geometry, 2018] that the answer is negative. In this paper, we show that none of the self-concordant barrier interior point methods is strongly polynomial. This result is obtained by establishing that, on parametric families of convex optimization problems, the log-limit of the central path degenerates to a piecewise linear curve, independently of the choice of the barrier function. We provide an explicit linear program that falls in the same class as the Klee-Minty counterexample for the simplex method, i.e., in which the feasible region is a combinatorial cube and the number of iterations is ω(2n).
KW - Interior point methods
KW - convex programming
KW - linear programming
KW - self-concordant barriers
KW - tropical geometry
U2 - 10.1145/3519935.3519997
DO - 10.1145/3519935.3519997
M3 - Conference contribution
AN - SCOPUS:85132720882
T3 - Proceedings of the Annual ACM Symposium on Theory of Computing
SP - 515
EP - 528
BT - STOC 2022 - Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing
A2 - Leonardi, Stefano
A2 - Gupta, Anupam
PB - Association for Computing Machinery
T2 - 54th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2022
Y2 - 20 June 2022 through 24 June 2022
ER -