Powerful Features for Complex Problems
GenusCore provides three specialized engines, each optimized for different types of graph and optimization problems.
DynamicGraph
High-performance dynamic graph processor with incremental shortest paths. The key advantage: after updating edges, you don't need to recompute from scratch — incremental updates are 1000x faster.
Update shortest paths in milliseconds, not minutes
2,788 edge updates per second on million-node graphs
Handles graphs with 10M+ nodes
G = DynamicGraph(num_nodes=1000)
# Build graph
G.add_edge(0, 1, weight=1.0)
G.add_edge(1, 2, weight=2.0)
# Full computation
distances = G.shortest_paths(source=0)
print(f"Distance 0→2: {distances[2]}")
# Update edge
G.update_edge(0, 1, weight=0.5)
# Incremental (1000x FASTER!)
new_dist = G.update_shortest_paths(source=0)
print(f"New distance: {new_dist[2]}")opt = SwarmOptimizer(num_nodes=100)
# Load graph
edges = [
(0, 1), (1, 2), (2, 3),
(3, 0), (0, 2), (1, 3)
]
opt.load_graph(edges)
# Solve Max-Cut
result = opt.solve_maxcut(iterations=1000)
print(f"Cut value: {result.objective_value}")
print(f"Partition: {result.solution}")
print(f"Time: {result.time_ms:.2f}ms")SwarmOptimizer
Advanced optimizer for NP-hard combinatorial problems. Uses our proprietary optimization techniques to find high-quality solutions for Max-Cut, graph partitioning, and QUBO problems.
Find optimal graph partitions for clustering and circuit design
Competitive with state-of-the-art solvers
100-node problems solved in ~1 second
IsingMachine
High-performance Ising machine for combinatorial optimization. Map your problems to Ising/QUBO formulations and find ground states using our proprietary algorithms.
Directly solve Quadratic Unconstrained Binary Optimization
Multiple solution attempts for better results
Tune damping and iterations for your problem
ising = IsingMachine(num_spins=100)
# Create frustrated lattice
edges = []
for i in range(100):
edges.append((i, (i + 1) % 100))
edges.append((i, (i + 2) % 100))
ising.load_graph(edges)
# Solve with parallel runs
result = ising.solve(
iterations=1000,
parallel_runs=4,
damping=0.1
)
print(f"Energy: {result.objective_value}")Enterprise-Grade Security
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