Prediction models for linear program right-hand sides are trained via decision error minimization and historical primal-dual solutions to ensure the true optimal solution remains feasible and optimal under the predicted constraints.
Scikit-learn: Machine learning in python.the Journal of machine Learning research, 12:2825–2830, 2011
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A survey of data-driven methods for materials modeling at nanoscale, mesoscale, and micro-to-continuum scales that identifies established capabilities, data quality issues, and obstacles to cross-scale integration.
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Decision-Aware Predictions for Right-Hand Side Parameters in Linear Programs
Prediction models for linear program right-hand sides are trained via decision error minimization and historical primal-dual solutions to ensure the true optimal solution remains feasible and optimal under the predicted constraints.
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Materials Informatics Across the Length Scales
A survey of data-driven methods for materials modeling at nanoscale, mesoscale, and micro-to-continuum scales that identifies established capabilities, data quality issues, and obstacles to cross-scale integration.