Beschreibung
InhaltsangabeSeeking Feasibility.- Preliminaries.- Seeking Feasibility in Linear Programs.- Seeking Feasibility in Mixed-Integer Linear Programs.- A Brief Tour of Constraint Programming.- Seeking Feasibility in Nonlinear Programs.- Analyzing Infeasibility.- Isolating Infeasibility.- Finding the Maximum Feasible Subset of Linear Constraints.- Altering Constraints to Achieve Feasibility.- Applications.- Other Model Analyses.- Data Analysis.- Miscellaneous Applications.- Epilogue.
Inhalt
Part I: Analyzing Infeasibility.- Isolating an Infeasibility.- Methods Specific to Linear Programming.- Methods Specific to Mixed Integer Programming.- Methods Specific to Nonlinear Programming.- Finding the Maximum Feasible Subset of Linear Constraints.- Finding the Best Fix for an Infeasible System.- Part II: Reaching Feasibility Quickly.- Linear Programming.- Mixed Integer Programming.- Nonlinear Programming.- Part III: Applications.- Analyzing Unboundedness in Linear Programs.- Analyzing the Viability of Network Models.- Analyzing Multiple-Objective Linear Programs.- Data Classification and Training Neural Networks.- Applications In Statistics.- Radiation Treatment Planning.- Backtracking in Constraint Programming.- Protein Folding.- Automatic Test Assembly.- General NP-Hard Problems.
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