hybrid constraint solver

Technique

A hybrid constraint solver is a constraint-solving technique described in the evidence as a solver for constrained random simulation in hardware verification, targeting mixed Boolean/integer variable domains and using Markov-chain Monte Carlo methods to obtain good performance and solution distribution.

First seen 7/17/2026

Last seen 7/17/2026

Evidence 1 chunks

Wiki v1

WIKI

hybrid constraint solver

Overview

A hybrid constraint solver is described in the evidence as a technique for efficient constraint solving in constrained random simulation. In that setting, input stimuli are generated randomly but must satisfy declaratively specified constraints before being used in simulation-based hardware verification.

Technique characteristics

The cited solver is proposed for mixed Boolean/integer variable domains and is based on Markov-chain Monte Carlo (MCMC) methods. The evidence emphasizes two key criteria for this kind of solver: runtime performance and the distribution of generated solutions.

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RELATIONSHIPS

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constraint solving implements → 1e

The hybrid constraint solver implements constraint solving for stimulus generation.

Markov-chain Monte Carlo uses → 1e

The hybrid constraint solver is based on Markov-chain Monte Carlo methods.

solution distribution evaluates → 1e

The hybrid constraint solver addresses the quality of solution distribution.

mixed Boolean/integer variable domains uses → 1e

The hybrid constraint solver operates over mixed Boolean/integer variable domains.

Stimulus Generation ← uses 1e

The hybrid constraint solver is used for stimulus generation in verification flows.

LINKED ENTITIES

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constraint solving IMPLEMENTS Extracted graph relationship

Markov-chain Monte Carlo USES Extracted graph relationship

solution distribution EVALUATES Extracted graph relationship

mixed Boolean/integer variable domains USES Extracted graph relationship

Stimulus Generation USES Extracted graph relationship

CITATIONS

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[1] A hybrid constraint solver is proposed for constrained random simulation in hardware verification. Stimulus generation for constrained random simulation

[2] The solver targets mixed Boolean/integer variable domains. Stimulus generation for constrained random simulation

[3] The solver is based on Markov-chain Monte Carlo methods. Stimulus generation for constrained random simulation

[4] Performance and the distribution of generated solutions are key criteria, and the proposed solver is reported to have good performance and distribution. Stimulus generation for constrained random simulation

[5] General-purpose hybrid constraint solvers can be powerful, but direct encodings may scale poorly on some problems, motivating problem-specific encodings. Solving Quantum-Inspired Perfect Matching Problems via Tutte's Theorem-Based Hybrid Boolean Constraints