Methods and systems for finding optimal or near optimal solutions for generic optimization problems by an approach to minimizing functions over high-dimensional domains that mathematically model the optimization problems. Embodiments of the disclosed invention receive a mathematical description of a
system, in symbolic form, that includes decision variables of various types, including real-number-valued, integer-valued, and Boolean-valued decision variables, and that may also include a variety of constraints on the values of the decision variables, including inequality and equality constraints. The objective function and constraints are incorporated into a global objective function. The global objective function is transformed into a
system of differential equations in terms of continuous variables and parameters, so that polynomial-time methods for solving differential equations can be applied to calculate near-optimal solutions for the global objective function. Embodiments of the present invention also provide for distribution and
decomposition of global-gradient-descent- field-based optimization methods, by following multiple trajectories, and local-gradient- descent-field-based optimization methods, by using multiple agents, in order to allow for parallel computation and increased computational efficiency. Various embodiments of the present invention further include approaches for relatively continuous adjustment of solutions to optimization problems in time, to respond to various events, changes in priorities, and changes in forecasts, without needing to continuously recalculate optimization solutions de novo. While many embodiments of the present invention are specifically directed to various classes of optimization problems, other embodiments of the present invention provide a more general approach for constructing complex hierarchical computational processes and for optimally or near optimally controlling general computational processes.