cooptimizing

Cooptimizing (also written co-optimizing) refers to optimizing two or more interacting components, objectives, or subsystems within a single framework to achieve better overall performance than optimizing them in isolation. It emphasizes the interdependencies among decisions and outcomes, where changes in one part influence others.

Common domains include multi-objective optimization and multi-agent systems, co-design of hardware and software, and compiler optimization where instruction selection, register allocation, and code layout are chosen together to balance runtime performance, energy use, and code size. In engineering and operations research, cooptimization models capture how subsystems such as energy generation and demand, or production and distribution, influence each other.

Methodologically, cooptimization uses mathematical programming (linear, integer, mixed-integer), bilevel or hierarchical optimization, and multi-objective approaches that seek Pareto-efficient solutions. Decomposition, coordination techniques, and accurate modeling of couplings are also important, as are surrogate models or machine learning methods to manage complexity and scale.

Challenges include non-convexity, scalability, and accurately representing interdependencies. Evaluations can be costly and time-consuming, requiring simulations or real-world measurements. The choice between pursuing a Pareto frontier or a single design with a blended objective via weighting depends on stakeholder goals and acceptable trade-offs. Cooptimization is often paired with co-design or joint decision-making, reflecting a shift from isolated optimization to integrated, system-wide reasoning.