GAMS

POUNCE plugs into GAMS as an NLP solver, so a model can hand its problem to POUNCE with:

option nlp = pounce;
solve mymodel using nlp minimizing obj;

There are two ways to make POUNCE available to GAMS. Pick one:

Both register POUNCE under the same name (pounce) for NLP, DNLP, and RMINLP models — POUNCE is a continuous local NLP solver, so mixed-integer and conic model types are not offered here.

The pip route

1. Install

pip install pounce-solver[gams]

The [gams] extra pulls in gamsapi[core] — GAMS’s own expert-level GMO/GEV Python bindings — and PyYAML. The bindings dlopen the GAMS C libraries from your local install, so gamsapi must match your GAMS version. POUNCE itself redistributes nothing GAMS-owned. If your GAMS and gamsapi versions disagree, install the matching one from your GAMS system (GAMS ships a gamsapi wheel under apifiles/Python/), or:

pip install 'gamsapi[core]==<your GAMS X.Y.Z>'

2. Check the install

pounce-gams status

reports whether gamsapi imports, the config directory POUNCE will register into, and whether POUNCE is already registered:

gamsapi:       available
               gamsapi 53.2.0 importable
config dir:    /Users/you/Library/Preferences/GAMS
gamsconfig:    /Users/you/Library/Preferences/GAMS/gamsconfig.yaml (missing)
POUNCE solver: not registered

3. Register

pounce-gams register

This writes a tiny launcher script and a solverConfig entry into your GAMS per-user gamsconfig.yaml. It merges — any other solvers already in that file (CONOPT overrides, discopt, …) are preserved — and is idempotent (re-running just updates POUNCE in place). The per-user config directory GAMS searches is OS-specific:

Override the target with --config-dir <path> (e.g. to register into the GAMS system directory instead). To undo, pounce-gams unregister.

No sudo is needed and nothing is written into the GAMS system directory, so a GAMS upgrade does not wipe the registration.

4. Solve

option nlp = pounce;
solve mymodel using nlp minimizing obj;

GAMS invokes the launcher with a control file; the launcher runs the Python link, which reads the model through GMO/GEV, solves it with POUNCE, and writes the primal/dual solution and GAMS model/solve status back.

Option files

If a model sets mymodel.optfile = 1, POUNCE reads pounce.opt (.op2, .op3, … for higher optfile values). Each line is a keyword value pair using POUNCE’s option names; lines starting with * or # are comments. The GAMS iterlim and reslim are honored as max_iter and max_wall_time.

* pounce.opt
tol        1e-10
max_iter   500

Machine-readable solve report

Set json_output in pounce.opt to also emit a structured pounce.solve-report/v1 JSON report (identical to the CLI’s --json-output, consumable by pounce-studio):

json_output  my_solve.json
json_detail  full        * "summary" or "full"; default is "full"

See the JSON Solve Report schema for the format.

Notes & limitations

• Version match. The single most common failure is a gamsapi ↔ GAMS version mismatch; pounce-gams status diagnoses it.
• Warm starts. The active-set-SQP working-set / state-file warm-start features (algorithm active-set-sqp, sqp_state_file) are currently only in the native C link, where each solve reuses an in-process state. The pip link runs each solve as a fresh process; full warm-start parity is a planned follow-up.