Skip to content
STIMSMITH

mixed Boolean/integer variable domains

Concept WIKI v1 · 7/17/2026

Mixed Boolean/integer variable domains are variable domains that combine Boolean and integer variables. In the cited hardware-verification work, they appear in the context of constrained random simulation, where efficient stimulus generation depends on solving constraints over such domains with good performance and solution distribution.

mixed Boolean/integer variable domains

Mixed Boolean/integer variable domains combine Boolean variables and integer variables within the same constraint-solving problem.

In the cited work on constrained random simulation for hardware verification, these domains are the setting for stimulus generation: input stimuli must be generated randomly while obeying declaratively specified input constraints. The paper frames efficient constraint solving for mixed Boolean/integer variable domains as an important part of the overall verification flow, and it presents a hybrid solver based on Markov-chain Monte Carlo methods for this purpose.

Relevance in constrained random simulation

According to the cited abstract, the efficiency of constrained random simulation depends critically on:

  1. the performance of the constraint solver, and
  2. the distribution of the generated solutions.

This makes mixed Boolean/integer variable domains relevant wherever stimulus-generation constraints span both logical conditions and integer-valued quantities.

LINKED ENTITIES

1 links
hybrid constraint solver USES

CITATIONS

3 sources
3 citations
[1] Mixed Boolean/integer variable domains combine Boolean and integer variables and are discussed as the domain for efficient constraint solving in the cited work. Stimulus generation for constrained random simulation
[2] In constrained random simulation, input stimuli are randomly generated subject to declaratively specified input constraints. Stimulus generation for constrained random simulation
[3] The cited work states that overall efficiency depends critically on solver performance and the distribution of generated solutions, and it proposes a hybrid solver based on Markov-chain Monte Carlo methods for mixed Boolean/integer variable domains. Stimulus generation for constrained random simulation