It is well-known how to use maximum flow to decide when a flow problem with demands, lower bounds, and upper bounds is infeasible. Less well-known is how to compute a flow that is least infeasible. This paper considers many possible ways to define “least infeasible” and shows how to compute optimal flows for each definition. For each definition it also gives a dual characterization in terms of ...

A novel matching based heuristic algorithm designed to detect specially formulated infeasible {0, 1} IPs is presented. The algorithm's input is a set of nested doubly stochastic subsystems and a set E of instance defining variables set at zero level. The algorithm deduces additional variables at zero level until either a constraint is violated (the IP is infeasible), or no more variables can be...

A two-class Support Vector Machine (SVM) classifier finds a hyperplane that separates two classes of data with the maximum margin. In a first learning phase, SVM involves the construction and solution of a primal-dual interiorpoint optimization problem. Each iteration of the interior-point method (IPM) requires a sparse linear system solution, which dominates the execution time of SVM learning....

In this paper the concept of the Minimum Universal Cost Flow (MUCF) for an infeasible flow network is introduced. A new mathematical model in which the objective function includes the total costs of changing arc capacities and sending flow is built and analyzed. A polynomial time algorithm is presented to find the MUCF.