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Facial reduction for symmetry reduced semidefinite and doubly nonnegative programs
Hao Hu1, Renata Sotirov2, Henry Wolkowicz3
1Clemson, South Carolina 29634 USA School of Mathematical and Statistical Sciences, Clemson University.
Facial reduction (FR) and symmetry reduction (SR) techniques enhance semidefinite programming (SDP) via alternating direction method of multipliers (ADMM). This approach improves numerical stability and running time for solving complex combinatorial problems.
Area of Science:
- Optimization
- Numerical Analysis
- Combinatorial Optimization
Background:
- Semidefinite programming (SDP) is a powerful tool for solving complex problems.
- Existing methods for SDP can be computationally intensive and suffer from numerical instability.
- Doubly nonnegative (DNN) relaxations are used for hard combinatorial problems.
Purpose of the Study:
- To integrate facial reduction (FR) and symmetry reduction (SR) techniques into an alternating direction method of multipliers (ADMM) framework.
- To solve doubly nonnegative (DNN) relaxations of hard combinatorial problems more efficiently.
- To improve the numerical stability and reduce the running time of SDP solvers.
Main Methods:
- Combining FR and SR techniques within an ADMM framework.
- Incorporating nonnegativity constraints to solve DNN relaxations.
- Analyzing the singularity degree of DNN relaxations before and after reduction.
Main Results:
- The integrated FR and SR approach fits well within the ADMM framework.
- The method allows for solving DNN relaxations of hard combinatorial problems, including large-scale quadratic assignment problems.
- Significant improvements in numerical stability and running time were observed for both ADMM and interior point methods.
Conclusions:
- The combination of FR and SR offers a significant advantage for solving DNN relaxations of combinatorial problems.
- The proposed method enhances both the efficiency and stability of SDP solvers.
- This approach is effective for large-scale problems with high-dimensional semidefinite constraints.
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