Globally optimizing mixed-integer quadratically-constrained quadratic programs. Major applications of mixed-integer quadratically-constrained quadratic programs (MIQCQP) include quality blending in process networks, separating objects in computational geometry, and portfolio optimization in finance. Specific instantiations of MIQCQP in process networks optimization problems include: pooling problems, distillation sequences, wastewater treatment and total water systems, hybrid energy systems, heat exchanger networks, reactor-separator-recycle systems, separation systems, data reconciliation, batch processes, crude oil scheduling, and natural gas production. Computational geometry problems formulated as MIQCQP include: point packing, cutting convex shapes from rectangles, maximizing the area of a convex polygon, and chip layout and compaction. Portfolio optimization in financial engineering can also be formulated as MIQCQP

References in zbMATH (referenced in 71 articles , 2 standard articles )

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  1. Nohra, Carlos J.; Raghunathan, Arvind U.; Sahinidis, Nikolaos: Spectral relaxations and branching strategies for global optimization of mixed-integer quadratic programs (2021)
  2. Bienstock, Daniel; Chen, Chen; Muñoz, Gonzalo: Outer-product-free sets for polynomial optimization and oracle-based cuts (2020)
  3. Ceccon, Francesco; Siirola, John D.; Misener, Ruth: SUSPECT: MINLP special structure detector for Pyomo (2020)
  4. Dey, Santanu S.; Kocuk, Burak; Santana, Asteroide: Convexifications of rank-one-based substructures in QCQPs and applications to the pooling problem (2020)
  5. Grimstad, Bjarne; Knudsen, Brage R.: Mathematical programming formulations for piecewise polynomial functions (2020)
  6. Gupte, Akshay; Kalinowski, Thomas; Rigterink, Fabian; Waterer, Hamish: Extended formulations for convex hulls of some bilinear functions (2020)
  7. Rebennack, Steffen; Krasko, Vitaliy: Piecewise linear function fitting via mixed-integer linear programming (2020)
  8. Santana, Asteroide; Dey, Santanu S.: The convex hull of a quadratic constraint over a polytope (2020)
  9. Xia, Wei; Vera, Juan C.; Zuluaga, Luis F.: Globally solving nonconvex quadratic programs via linear integer programming techniques (2020)
  10. Adams, Warren; Gupte, Akshay; Xu, Yibo: Error bounds for monomial convexification in polynomial optimization (2019)
  11. Bonami, Pierre; Lodi, Andrea; Schweiger, Jonas; Tramontani, Andrea: Solving quadratic programming by cutting planes (2019)
  12. Elloumi, Sourour; Lambert, Amélie: Global solution of non-convex quadratically constrained quadratic programs (2019)
  13. Furini, Fabio; Traversi, Emiliano; Belotti, Pietro; Frangioni, Antonio; Gleixner, Ambros; Gould, Nick; Liberti, Leo; Lodi, Andrea; Misener, Ruth; Mittelmann, Hans; Sahinidis, Nikolaos V.; Vigerske, Stefan; Wiegele, Angelika: QPLIB: a library of quadratic programming instances (2019)
  14. Göttlich, S.; Potschka, A.; Teuber, C.: A partial outer convexification approach to control transmission lines (2019)
  15. Lu, Cheng; Deng, Zhibin; Zhou, Jing; Guo, Xiaoling: A sensitive-eigenvector based global algorithm for quadratically constrained quadratic programming (2019)
  16. Nagarajan, Harsha; Lu, Mowen; Wang, Site; Bent, Russell; Sundar, Kaarthik: An adaptive, multivariate partitioning algorithm for global optimization of nonconvex programs (2019)
  17. Pecci, Filippo; Abraham, Edo; Stoianov, Ivan: Global optimality bounds for the placement of control valves in water supply networks (2019)
  18. Baltean-Lugojan, Radu; Misener, Ruth: Piecewise parametric structure in the pooling problem: from sparse strongly-polynomial solutions to NP-hardness (2018)
  19. Berthold, Timo: A computational study of primal heuristics inside an MI(NL)P solver (2018)
  20. Bonami, Pierre; Günlük, Oktay; Linderoth, Jeff: Globally solving nonconvex quadratic programming problems with box constraints via integer programming methods (2018)

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