The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Interior Point Methods in Semidefinite Programming with Applications to Combinatorial Optimization
Abstract
This paper studies the semidefinite programming SDP problem, i.e., the optimization problem of a linear function of a symmetric matrix subject to linear equality constraints and the additional condition that the matrix be positive semidefinite. First the classical cone duality is reviewed as it is specialized to SDP is reviewed. Next an interior point algorithm is presented that converges to the optimal solution in polynomial time. The approach is a direct extension of Ye’s projective method for linear programming. It is also argued that many known interior point methods for linear programs can be transformed in a mechanical way to algorithms for SDP with proofs of convergence and polynomial time complexity carrying over in a similar fashion. Finally, the significance of these results is studied in a variety of combinatorial optimization problems including the general 0-1 integer programs, the maximum clique and maximum stable set problems in perfect graphs, the maximum k-partite subgraph problem in graphs, and various graph partitioning and cut problems. As a result, barrier oracles are presented for certain combinatorial optimization problems (in particular, clique and stable set problem for perfect graphs) whose linear programming formulation requires exponentially many inequalities. Existence of such barrier oracles refutes the commonly believed notion that to solve a combinatorial optimization problem with interior point methods, its linear programming formulation is eeded explicitly.
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Cites
Rockafellar, R. T. (1970) · Princeton University Press eBooks · doi:10.1515/9781400873173 · Cited by 10,801
Horn, R. A., & Johnson, C. R. (1985) · Cambridge University Press eBooks · doi:10.1017/cbo9780511810817 · Cited by 10,280
A new polynomial-time algorithm for linear programming
Karmarkar, N. (1984) · Combinatorica · doi:10.1007/bf02579150 · Cited by 4,794
Interior-Point Polynomial Algorithms in Convex Programming
Nesterov, Y., & Nemirovskii, A. (1994) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611970791 · Cited by 4,334
Goemans, M. X., & Williamson, D. P. (1995) · Journal of the ACM · doi:10.1145/227683.227684 · Cited by 3,587
Geometric Algorithms and Combinatorial Optimization
Grötschel, M., Lovász, L., & Schrijver, A. (1988) · Algorithms and Combinatorics · doi:10.1007/978-3-642-97881-4 · Cited by 3,431
Theory of Linear and Integer Programming
Smith, D. K. (1987) · Journal of the Operational Research Society · doi:10.1057/jors.1987.94 · Cited by 2,708
The ellipsoid method and its consequences in combinatorial optimization
Grötschel, M., Lovász, L., & Schrijver, A. (1981) · Combinatorica · doi:10.1007/bf02579273 · Cited by 2,011
On the Shannon capacity of a graph
Lovasz, L. (1979) · IEEE Transactions on Information Theory · doi:10.1109/tit.1979.1055985 · Cited by 1,624
Cones of Matrices and Set-Functions and 0–1 Optimization
Lovász, L., & Schrijver, A. (1991) · SIAM Journal on Optimization · doi:10.1137/0801013 · Cited by 1,008
Lower Bounds for the Partitioning of Graphs
Donath, W. E., & Hoffman, A. J. (1973) · IBM Journal of Research and Development · doi:10.1147/rd.175.0420 · Cited by 660
Pathways to the Optimal Set in Linear Programming
Megiddo, N. (1989) · Progress in Mathematical Programming · doi:10.1007/978-1-4613-9617-8_8 · Cited by 588
A Primal-Dual Interior Point Algorithm for Linear Programming
Kojima, M., Mizuno, S., & Yoshise, A. (1989) · Springer eBooks · doi:10.1007/978-1-4613-9617-8_2 · Cited by 517
Interior path following primal-dual algorithms. part I: Linear programming
Monteiro, R. D. C., & Adler, I. (1989) · Mathematical Programming · doi:10.1007/bf01587075 · Cited by 486
Matching is as easy as matrix inversion
Mulmuley, K., Vazirani, U. V., & Vazirani, V. V. (1987) · Combinatorica · doi:10.1007/bf02579206 · Cited by 388
An Algorithm for Partitioning the Nodes of a Graph
Barnes, E. R. (1982) · SIAM Journal on Algebraic Discrete Methods · doi:10.1137/0603056 · Cited by 299
Large-Scale Optimization of Eigenvalues
Overton, M. L. (1992) · SIAM Journal on Optimization · doi:10.1137/0802007 · Cited by 209
On Minimizing the Maximum Eigenvalue of a Symmetric Matrix
Overton, M. L. (1988) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/0609021 · Cited by 206
Overton, M. L., & Womersley, R. S. (1993) · Mathematical Programming · doi:10.1007/bf01585173 · Cited by 189
Laplacian eigenvalues and the maximum cut problem
Delorme, C., & Poljak, S. (1993) · Mathematical Programming · doi:10.1007/bf01585184 · Cited by 179
The Formulation and Analysis of Numerical Methods for Inverse Eigenvalue Problems
Friedland, S., Nocedal, J., & Overton, M. L. (1987) · SIAM Journal on Numerical Analysis · doi:10.1137/0724043 · Cited by 163
Advances in Optimization and Parallel Computing
Pardalos, P. M. (1992) · Elsevier eBooks · Cited by 146
The minimization of certain nondifferentiable sums of eigenvalues of symmetric matrices
Cullum, J. K., Donath, W. E., & Wolfe, P. (1975) · Mathematical programming studies · doi:10.1007/bfb0120698 · Cited by 122
On the Sum of the Largest Eigenvalues of a Symmetric Matrix
Overton, M. L., & Womersley, R. S. (1992) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/0613006 · Cited by 115
An Interior-Point Method for Minimizing the Maximum Eigenvalue of a Linear Combination of Matrices
Jarre, F. (1993) · SIAM Journal on Control and Optimization · doi:10.1137/0331064 · Cited by 106
Generalizations of Farkas’ Theorem
Craven, B. D., & Koliha, J. J. (1977) · SIAM Journal on Mathematical Analysis · doi:10.1137/0508076 · Cited by 84
Some applications of optimization in matrix theory
Wolkowicz, H. (1981) · Linear Algebra and its Applications · doi:10.1016/0024-3795(81)90143-9 · Cited by 62
Cited by
Goemans, M. X., & Williamson, D. P. (1995) · Journal of the ACM · doi:10.1145/227683.227684 · Cited by 3,587
Disciplined Convex Programming
Grant, M., Boyd, S., & Ye, Y. (2006) · Nonconvex Optimization and Its Applications · doi:10.1007/0-387-30528-9_7 · Cited by 1,026
A Direct Formulation for Sparse PCA Using Semidefinite Programming
d'Aspremont, A., El Ghaoui, L., Jordan, M. I., & Lanckriet, G. R. G. (2007) · SIAM Review · doi:10.1137/050645506 · Cited by 678
Domain Adaptation: Learning Bounds and Algorithms
Mansour, Y., Mohri, M., & Rostamizadeh, A. (2009) · arXiv · doi:10.48550/arxiv.0902.3430 · Cited by 504
Knuth, D. E. (1994) · The Electronic Journal of Combinatorics · doi:10.37236/1193 · Cited by 296
Metadata
- Authors
- Alizadeh, F.
- Venue
- SIAM Journal on Optimization
- Published
- 1995-02
- Volume
- 5
- Issue
- 1
- Pages
- 13-51
- ISSN
- 1052-6234 (ISSN-L)
- 1095-7189
- OpenAlex
- W1963753244
- Cited by
- 892
- References
- 27
- FWCI
- 36.88
- Type
- journal article
- Language
- en
- Open access
- closed
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