Transforming Optimization Problems into Disciplined Convex Programming Form
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Springer Nature Switzerland
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https://link.springer.com/content/pdf/10.1007/978-3-031-66997-2_11
Reference38 articles.
1. Agrawal, A., Boyd, S.: Disciplined quasiconvex programming. Optim. Lett. 14, 1643–1657 (2020). https://doi.org/10.1007/s11590-020-01561-8
2. Agrawal, A., Diamond, S., Boyd, S.: Disciplined geometric programming. Optim. Lett. 13, 961–976 (2019). https://doi.org/10.1007/s11590-019-01422-z
3. Andersen, E.D., Andersen, K.D.: The MOSEK interior point optimizer for linear programming: an implementation of the homogeneous algorithm. In: Frenk, H., Roos, K., Terlaky, T., Zhang, S. (eds.) High Performance Optimization, pp. 197–232. Springer, Heidelberg (2000). https://doi.org/10.1007/978-1-4757-3216-0_8
4. Bentkamp, A., Fernández Mir, R., Avigad, J.: Verified reductions for optimization. In: Sankaranarayanan, S., Sharygina, N. (eds.) International Conference on Tools and Algorithms for the Construction and Analysis of Systems, pp. 74–92. Springer, Heidelberg (2023). https://doi.org/10.1007/978-3-031-30820-8_8
5. Boyd, S.P., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004). https://doi.org/10.1017/CBO9780511804441
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