No Cross-Validation Required: An Analytical Framework for Regularized Mixed-Integer Problems

This letter develops a method to obtain the optimal value for the regularization coefficient in a general mixed-integer problem (MIP). This approach eliminates the cross-validation performed in the existing penalty techniques to obtain a proper value for the regularization coefficient. We obtain thi...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE communications letters Ročník 24; číslo 12; s. 2868 - 2872
Hlavní autoři: Soleimani, Behrad, Khamidehi, Behzad, Sabbaghian, Maryam
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
alternating method
Closed-form solutions
Coefficients
Convergence
Heterogeneous networks
Iterative algorithms
Iterative methods
Linear programming
Lower bounds
Mixed integer
Mixed-integer programming
Optimization
penalty function
RAT selection
Regularization
Simulation
Upper bound
Upper bounds
ISSN:1089-7798, 1558-2558
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This letter develops a method to obtain the optimal value for the regularization coefficient in a general mixed-integer problem (MIP). This approach eliminates the cross-validation performed in the existing penalty techniques to obtain a proper value for the regularization coefficient. We obtain this goal by proposing an alternating method to solve MIPs. First, via regularization, we convert the MIP into a more mathematically tractable form. Then, we develop an iterative algorithm to update the solution along with the regularization (penalty) coefficient. We show that our update procedure guarantees the convergence of the algorithm. Moreover, assuming the objective function is continuously differentiable, we derive the convergence rate, a lower bound on the value of regularization coefficient, and an upper bound on the number of iterations required for the convergence. We use a radio access technology (RAT) selection problem in a heterogeneous network to benchmark the performance of our method. Simulation results demonstrate near-optimality of the solution and consistency of the convergence behavior with obtained theoretical bounds.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2020.3013377