Bemporad, Alberto Solving Mixed-Integer Quadratic Programs via Nonnegative Least Squares. IFAC-PapersOnLine, 48 (23). pp. 73-79. ISSN 24058963 (2015)
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Abstract
This paper proposes a new algorithm for solving Mixed-Integer Quadratic Programming (MIQP) problems. The algorithm is particularly tailored to solving small-scale MIQPs such as those that arise in embedded hybrid Model Predictive Control (MPC) applications. The approach combines branch and bound (B&B) with nonnegative least squares (NNLS), that are used to solve Quadratic Programming (QP) relaxations. The QP algorithm extends a method recently proposed by the author for solving strictly convex QP's, by (i) handling equality and bilateral inequality constraints, (ii) warm starting, and (iii) exploiting easy-to-compute lower bounds on the optimal cost to reduce the number of QP iterations required to solve the relaxed problems. The proposed MIQP algorithm has a speed of execution that is comparable to state- of-the-art commercial MIQP solvers and is relatively simple to code, as it requires only basic arithmetic operations to solve least-square problems.
| Item Type: | Article |
|---|---|
| Identification Number: | 10.1016/j.ifacol.2015.11.264 |
| Additional Information: | 5th IFAC Conference on Nonlinear Model Predictive Control NMPC 2015 - Seville, Spain, 17–20 September 2015 |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) |
| Research Area: | Computer Science and Applications |
| Depositing User: | Caterina Tangheroni |
| Date Deposited: | 26 Jan 2017 14:29 |
| Last Modified: | 26 Jan 2017 14:29 |
| URI: | http://eprints.imtlucca.it/id/eprint/3642 |
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