Extends variable projection to constrained separable nonlinear least-squares via bilevel collapse, yielding exact reduced gradients and a convergent conditional-gradient algorithm.
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Adaptive multi-criteria scoring with online logistic regression for Benders subproblem selection yields statistically significant runtime and integral improvements on 135 survivable network design instances.
Augmenting SAT solvers with the Euler-Parker algorithm solves hard 10x10 orthogonal Latin square problems in a median of 5100 seconds instead of failing after seven days.
A neurodynamic duplex neural network method solves distributionally robust geometric joint chance-constrained problems by converging in probability to the global optimum via projection equations.
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Adaptive Subproblem Selection in Benders Decomposition for Survivable Network Design Problems
Adaptive multi-criteria scoring with online logistic regression for Benders subproblem selection yields statistically significant runtime and integral improvements on 135 survivable network design instances.