Table of Contents
Fetching ...

Quantale-Enriched Co-Design: Toward a Framework for Quantitative Heterogeneous System Design

Hans Riess, Yujun Huang, Matthew Klawonn, Gioele Zardini, Matthew Hale

Abstract

Monotone co-design enables compositional engineering design by modeling components through feasibility relations between required resources and provided functionalities. However, its standard boolean formulation cannot natively represent quantitative criteria such as cost, confidence, or implementation choice. In practice, these quantities are often introduced through ad hoc scalarization or by augmenting the resource space, which obscures system structure and increases computational burden. We address this limitation by developing a quantale-enriched theory of co-design. We model resources and functionalities as quantale-enriched categories and design problems as quantale-enriched profunctors, thereby lifting co-design from boolean feasibility to general quantitative evaluation. We show that the fundamental operations of series, parallel, and feedback composition remain valid over arbitrary commutative quantales. We further introduce heterogeneous composition through change-of-base maps between quantales, enabling different subsystems to be evaluated in different local semantics and then composed in a common framework. The resulting theory unifies feasibility-, cost-, confidence-, and implementation-aware co-design within one compositional formalism. Numerical examples on a target-tracking system and a UAV delivery problem demonstrate the framework and highlight how native quantitative enrichment can avoid the architectural and computational drawbacks of boolean-only formulations.

Quantale-Enriched Co-Design: Toward a Framework for Quantitative Heterogeneous System Design

Abstract

Monotone co-design enables compositional engineering design by modeling components through feasibility relations between required resources and provided functionalities. However, its standard boolean formulation cannot natively represent quantitative criteria such as cost, confidence, or implementation choice. In practice, these quantities are often introduced through ad hoc scalarization or by augmenting the resource space, which obscures system structure and increases computational burden. We address this limitation by developing a quantale-enriched theory of co-design. We model resources and functionalities as quantale-enriched categories and design problems as quantale-enriched profunctors, thereby lifting co-design from boolean feasibility to general quantitative evaluation. We show that the fundamental operations of series, parallel, and feedback composition remain valid over arbitrary commutative quantales. We further introduce heterogeneous composition through change-of-base maps between quantales, enabling different subsystems to be evaluated in different local semantics and then composed in a common framework. The resulting theory unifies feasibility-, cost-, confidence-, and implementation-aware co-design within one compositional formalism. Numerical examples on a target-tracking system and a UAV delivery problem demonstrate the framework and highlight how native quantitative enrichment can avoid the architectural and computational drawbacks of boolean-only formulations.

Paper Structure

This paper contains 5 sections, 1 theorem, 9 equations, 1 figure.

Key Result

Lemma 1

A $\mathsf{Q}$-design problem is equivalent to the data of a tuple $(\mathsf{R},\mathsf{F},\mathsf{Q},d)$ such that $d: \mathop{\mathrm{obj}}\nolimits(\mathsf{R}) \times \mathop{\mathrm{obj}}\nolimits(\mathsf{F}) \to \mathsf{Q}$ is a function satisfying for all $f,f^\ast \in \mathsf{F}$ and for all $r,r^\ast \in \mathsf{R}$, where $[-,-]$ is the internal hom (e.g., abramskyQuantalesObservationalL

Figures (1)

  • Figure 3: Monotone co-design does not natively support non-boolean design evaluations; state-of-practice approaches such as uncurrying (a) obscure the underlying design problem compared with quantale-enriched modeling (b).

Theorems & Definitions (12)

  • Definition 1
  • Example 1: Boolean Feasibility
  • Example 2: Hierarchical Feasibility
  • Example 3: Cost
  • Example 4: Confidence
  • Example 5: Implementation
  • Definition 2
  • Example 6: Orders
  • Example 7: Metrics
  • Example 8: Tesnor Product
  • ...and 2 more