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Semidefinite Programming

1996/03/01 by Lieven Vandenberghe, Stephen Boyd · 4,108 citations
Computer Science · Engineering · Mathematics · #Advanced Optimization Algorithms Research #Affine transformation #Computer science #Conic optimization #Convex analysis #Convex optimization #Interior point method #Linear matrix inequality #Linear programming #Mathematical optimization #Mathematics #Matrix Theory and Algorithms #Nonlinear programming #Nonlinear system #Pure mathematics #Quadratic programming #Quadratically constrained quadratic program #Regular polygon #Second-order cone programming #Semidefinite embedding #Semidefinite programming #Sparse and Compressive Sensing Techniques

paper · doi:10.1137/1038003

published in SIAM Review 38(1), 49-95 (Society for Industrial and Applied Mathematics)

openalex publication_date 1996/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/25

Abstract

In semidefinite programming one minimizes a linear function subject to the constraint that an affine combination of symmetric matrices is positive semidefinite. Such a constraint is nonlinear and nonsmooth, but convex, so semidefinite programs are convex optimization problems. Semidefinite programming unifies several standard problems (e.g., linear and quadratic programming) and finds many applications in engineering and combinatorial optimization. Although semidefinite programs are much more general than linear programs, they are not much harder to solve. Most interior-point methods for linear programming have been generalized to semidefinite programs. As in linear programming, these methods have polynomial worst-case complexity, and perform very well in practice. This paper gives a survey of the theory and applications of semidefinite programs, and an introduction to primal-dual interior-point methods for their solution.

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