A hierarchy of relaxation between the continuous and convex hull representations
SIAM Journal on Discrete Mathematics
A lift-and-project cutting plane algorithm for mixed 0-1 programs
Mathematical Programming: Series A and B
Approximation of the Stability Number of a Graph via Copositive Programming
SIAM Journal on Optimization
Global Optimization with Polynomials and the Problem of Moments
SIAM Journal on Optimization
An Explicit Equivalent Positive Semidefinite Program for Nonlinear 0-1 Programs
SIAM Journal on Optimization
Semidefinite Programming vs. LP Relaxations for Polynomial Programming
Mathematics of Operations Research
A Comparison of the Sherali-Adams, Lovász-Schrijver, and Lasserre Relaxations for 0--1 Programming
Mathematics of Operations Research
Sparsity in sums of squares of polynomials
Mathematical Programming: Series A and B
LMI Approximations for Cones of Positive Semidefinite Forms
SIAM Journal on Optimization
SIAM Journal on Optimization
Minimizing Polynomials via Sum of Squares over the Gradient Ideal
Mathematical Programming: Series A and B
Semidefinite representations for finite varieties
Mathematical Programming: Series A and B
The quadratic knapsack problem-a survey
Discrete Applied Mathematics
Bounds for the quadratic assignment problem using the bundle method
Mathematical Programming: Series A and B
Reduction of symmetric semidefinite programs using the regular $$\ast$$-representation
Mathematical Programming: Series A and B
ACM Transactions on Mathematical Software (TOMS)
A Branch and Bound Algorithm for Max-Cut Based on Combining Semidefinite and Polyhedral Relaxations
IPCO '07 Proceedings of the 12th international conference on Integer Programming and Combinatorial Optimization
A note on sparse SOS and SDP relaxations for polynomial optimization problems over symmetric cones
Computational Optimization and Applications
Recognizing underlying sparsity in optimization
Mathematical Programming: Series A and B
Mathematical Programming: Series A and B
Sparse SOS Relaxations for Minimizing Functions that are Summations of Small Polynomials
SIAM Journal on Optimization
SIAM Review
Second-Order Cone Relaxations for Binary Quadratic Polynomial Programs
SIAM Journal on Optimization
Mathematical Programming: Series A and B - Special Issue on Cone Programming and its Applications
Exploiting equalities in polynomial programming
Operations Research Letters
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Semidefinite programming has been used successfully to build hierarchies of convex relaxations to approximate polynomial programs. This approach rapidly becomes computationally expensive and is often tractable only for problems of small sizes. We propose an iterative scheme that improves the semidefinite relaxations without incurring exponential growth in their size. The key ingredient is a dynamic scheme for generating valid polynomial inequalities for general polynomial programs. These valid inequalities are then used to construct better approximations of the original problem. As a result, the proposed scheme is in principle scalable to large general combinatorial optimization problems. For binary polynomial programs, we prove that the proposed scheme converges to the global optimal solution for interesting cases of the initial approximation of the problem. We also present examples illustrating the computational behaviour of the scheme and compare it to other methods in the literature.