Partitioned iterative convergance programming model

A system, comprising:

• one or more global administrator nodes configured to partition a model and input data into sub-problems, comprising:
• a processor configured to determine whether a merged model, formed from a plurality of locally updated models, satisfies a global convergence criterion and to initiate a new global iteration if the global convergence criterion is not satisfied;
• and
• a plurality of local nodes configured to perform iterative convergence computations, comprising:
• a processor configured to iterate a computation on a partitioned sub-problem until a local convergence criterion has been satisfied, producing a locally updated model.

A method for partitioned iterative convergence comprising:

• performing at least one global iteration, said global iteration comprising:
• partitioning input data into a plurality of interdependent input data partitions according to an input data partitioning function;
• partitioning a model into a plurality of model partitions according to a model partitioning function;
• performing a plurality of parallel local iterations, comprising
• computing a sub-problems formed from a model partition and an input data partition using a processor to produce a locally updated model;
• and
• determining whether to perform a subsequent local iteration based on a local convergence criterion that considers a locally updated model;
• combining the plurality of locally updated models from the plurality of parallel local iterations according to a model merging function to produce a merged model;
• and
• determining whether to perform a subsequent global iteration based on a global convergence criterion that considers the merged model.