Skip to content
STIMSMITH

Maximal Convex Subgraph Enumeration

Technique
First seen 8/2/2026
Last seen 8/2/2026
Evidence 11 chunks

NEIGHBORHOOD

No graph connections found for this entity yet. It may appear in future ingestion runs.

explore full graph →

RELATIONSHIPS

12 connections
Integer Linear Programming (ILP) for Custom Instruction Synthesis ← compares with 100% 2e
ILP and enumeration are compared as complementary solution techniques
Forbidden Node uses → 100% 2e
The enumeration algorithm identifies forbidden nodes that cannot be included in custom instructions
Li et al. Search Tree Enumeration Algorithm ← compares with 95% 2e
Li et al.'s algorithm is compared with FISH's maximal convex subgraph enumeration in runtime experiments
Pothineni et al. Maximal Convex Subgraph Enumeration ← compares with 90% 2e
FISH's enumeration algorithm extends and compares with Pothineni et al.'s algorithm
Graph Compaction and Clustering uses → 100% 2e
The enumeration algorithm uses graph compaction and clustering to reduce search space
Search Tree with Constraint Propagation uses → 100% 2e
The enumeration algorithm builds a search tree and applies constraint propagation
Data Flow Graph (DFG) uses → 100% 2e
Maximal convex subgraph enumeration operates on data flow graphs
Convex Subgraph uses → 100% 2e
Maximal convex subgraph enumeration identifies convex subgraphs in a DFG
Merit Function for Subgraph Evaluation uses → 90% 1e
The enumeration algorithm integrates any user-defined merit function
FISH (Fast Instruction SyntHesis) ← uses 100% 1e
FISH uses maximal convex subgraph enumeration to identify custom instruction candidates
Bron-Kerbosch Maximal Clique Enumeration ← compares with 95% 1e
The enumeration algorithm is compared in runtime with Bron-Kerbosch clique enumeration
Pothineni et al. Maximal Convex Subgraph Enumeration ← introduces 100% 1e
Pothineni et al. were the first to target maximal convex subgraph enumeration