It was an American jurist who called the law "the wise constraints that make us free".
Another difficulty is the terminal state constraint, which is a sample-wise constraint.
The literature [14] proposed a distributed spectrum-aware clustering (DSAC) algorithm based on the traditional K-means clustering algorithm with group-wise constraint.
The constraints are provided in the form of pair-wise constraints: must- link and cannot- link.
We introduce a heuristic, practical procedure to take into account, in an easy-to-implement way, point-wise constraints in a variational problem in several dimensions.
Case-wise constraints can be applied to this heatmap as per the specifications of the project e.g.: the cost of external data is fixed at a particular value, and/or only batched external data is available.
We used 20 pair-wise constraints for CNS, Leukemia1, Leukemia2 and Leukemia3, but 100 constraints for other 4 datasets (Table 4).
The idea behind clustering with constraints, which is a semi-supervised learning method (Castelli and Cover, 1994; Chapelle et al., 2006), is to use pair-wise constraints between patients in order to restrict or penalize particular solutions.
These are interpreted as team-wise precedence constraints, and compatible with the precedence constraints.
As interpreted in Ji and Zhou [21], the stochastic control involving sample-wise state constraints cannot be resolved by the classical theory.
The way in which the decoupling is achieved allows practical problems characterized by hundreds of component-wise reliability constraints and high-dimensional discrete design variable vectors to be efficiently and rigorously solved.
