Abstract: A method of determining a conditional computational plan for a data dependent constraint network represented by a bipartite graph containing input variable nodes, output variable nodes, and relation nodes, may include specifying, using a variable node specifier, at least one output variable node for which a plan is desired. The method may further include determining, using a plan determiner, a plan from the input variable nodes to the output variable node using a backward chaining search of the bipartite graph.

Abstract: Multi-Objective Optimization (MOO) is integrated with a Constraint Management System (CMS) so as to rapidly and flexibly search large design spaces and focus on “interesting” designs as determined by user-specified criteria. A method embeds a trade space and its trade-off envelope within the same CMS network used to calculate the values of the points in the trade space itself. The method supports automatic variation of the resulting trade-off analyses in response to variations in upstream parameters, assumptions, and/or requirements. When feasible, the CMS can back-solve for values of upstream parameters so that selected attributes of the trade-off envelope achieve user-specified values. The coupled use of a CMS with multi-objective optimization avoids having to generate a large set of permutations of inputs and factors and then restricting the analysis to those having desired outputs or of having to manually “reformulate” the equation set so that it solves in the desired direction.