scholarly journals Reinforcement Learning for Variable Selection in a Branch and Bound Algorithm

2020 ◽  
pp. 176-185
Author(s):  
Marc Etheve ◽  
Zacharie Alès ◽  
Côme Bissuel ◽  
Olivier Juan ◽  
Safia Kedad-Sidhoum
Keyword(s):  
Reinforcement Learning ◽  
Variable Selection ◽  
Branch And Bound ◽  
Branch And Bound Algorithm

Optimal Network Problem: A Branch-and-Bound Algorithm

1973 ◽  
Vol 5 (4) ◽  
pp. 519-533 ◽  
Author(s):  
D E Boyce ◽  
A Farhi ◽  
R Weischedel
Keyword(s):  
Variable Selection ◽  
Branch And Bound ◽  
Shortest Path ◽  
Budget Constraint ◽  
Computational Experience ◽  
Branch And Bound Algorithm ◽  
Selection Problems ◽  
Network Problem ◽  
Optimal Network ◽  
Modified Algorithm

The problem of selecting a subset of links so as to minimize the sum of shortest path distances between all pairs of nodes, subject to a budget constraint on total length of links, may be solved by a modification of a branch-and-bound algorithm developed for optimal variable selection problems in statistics. The modified algorithm is described in detail, and encouraging computational experience on 10 node networks is reported. The use of the algorithm as a heuristic approach to solving the optimal network problem is also discussed.

Unit Commitment Solution by Branch and Bound Algorithm

2020 ◽  
Author(s):  
Bishaljit Paul ◽  
Sushovan Goswami ◽  
Dipu Mistry ◽  
Chandan Kumar Chanda
Keyword(s):  
Branch And Bound ◽  
Unit Commitment ◽  
Branch And Bound Algorithm

scholarly journals Identification of mechanical properties of arteries with certification of global optimality

2021 ◽  
Author(s):  
Jan-Lucas Gade ◽  
Carl-Johan Thore ◽  
Jonas Stålhand
Keyword(s):  
Global Solution ◽  
Branch And Bound ◽  
Clinical Data ◽  
Minimization Problem ◽  
Branch And Bound Algorithm ◽  
Stationary Points ◽  
Model Parameters ◽  
Global Optimality ◽  
Clinical Value ◽  
Non Linear

AbstractIn this study, we consider identification of parameters in a non-linear continuum-mechanical model of arteries by fitting the models response to clinical data. The fitting of the model is formulated as a constrained non-linear, non-convex least-squares minimization problem. The model parameters are directly related to the underlying physiology of arteries, and correctly identified they can be of great clinical value. The non-convexity of the minimization problem implies that incorrect parameter values, corresponding to local minima or stationary points may be found, however. Therefore, we investigate the feasibility of using a branch-and-bound algorithm to identify the parameters to global optimality. The algorithm is tested on three clinical data sets, in each case using four increasingly larger regions around a candidate global solution in the parameter space. In all cases, the candidate global solution is found already in the initialization phase when solving the original non-convex minimization problem from multiple starting points, and the remaining time is spent on increasing the lower bound on the optimal value. Although the branch-and-bound algorithm is parallelized, the overall procedure is in general very time-consuming.

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