Constrained optimization models are core tools in business, science, government, and the military with applications including airline scheduling, control of petroleum refining operations, investment decisions, and many others. Constrained optimization models have grown immensely in scale and complexity in recent years as inexpensive computing power has become widely available. Models now frequently have many complicated interacting constraints, giving rise to a host of issues related to feasibility and infeasibility. For example, it is sometimes difficult to find any feasible point at all for a large model, or even to accurately determine if one exists, e.g. for nonlinear models. If the model is feasible, how quickly can a solution be found? If the model is infeasible, how can the cause be isolated and diagnosed? Can a repair to restore feasibility be carried out automatically? Researchers have developed numerous algorithms and computational methods in recent years to address such issues, with a number of surprising spin-off applications in fields such as artificial intelligence and computational biology. Over the same time period, related approaches and techniques relating to feasibility and infeasibility of constrained problems have arisen in the constraint programming community.

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Seeking 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.