Admiral
Combinatorial optimization platform. 9 problem types, 27 proprietary algorithms, solver racing, AI advisor, and natural language formulation — all behind a single API.
Install
pip install admiral-solver
Quick Start
from admiral import Solver
solver = Solver(api_key="adm_sk_...")
# Solve a QUBO problem
result = solver.solve_qubo(Q=[[-5, 2, 4], [0, -3, 1], [0, 0, -8]])
print(result.energy) # -13.0
print(result.solution) # [1, 0, 1]
Problem Types
| Type | Description |
|---|---|
| QUBO | Quadratic unconstrained binary optimization |
| Ising | Spin glass models |
| HUBO | Higher-order binary optimization |
| DQM | Discrete quadratic models |
| CQM | Constrained quadratic models |
| MAX-SAT | Maximum satisfiability |
| PBO | Pseudo-boolean optimization |
| WCSP | Weighted constraint satisfaction |
| Potts | Multi-state lattice models |
Solver Methods
solver.solve_qubo(Q, timeout=30, num_reads=100)
solver.solve_ising(h, J, timeout=30, num_reads=100)
solver.solve_hubo(terms, num_variables, timeout=30)
solver.solve_dqm(domains, linear, quadratic, timeout=30)
solver.solve_cqm(c, Q, A, b, var_types, bounds, timeout=30)
solver.solve_maxsat(clauses, num_variables, timeout=30)
solver.solve_pbo(objective, constraints, num_variables, timeout=30)
solver.solve_wcsp(num_variables, domains, functions, timeout=30)
solver.solve_potts(num_nodes, num_states, edges, timeout=30)
Features
- Solver Racing — Race multiple solvers in parallel, return the best
- AI Advisor — Automatic solver recommendation
- NL Builder — Describe problems in English, get a formulation
- Explainability — Understand why a solution is optimal
Links
License
Apache 2.0
Metadata
Release files for admiral-solver 2.0.3
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| admiral_solver-2.0.3-py3-none-any.whl | Python 3 | none | any | Details |
Release files / admiral_solver-2.0.3-py3-none-any.whl
| Download URL | admiral_solver-2.0.3-py3-none-any.whl |
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| Size | 13.6 kB |
| Tags | Python 3 |
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