use std::cell::RefCell;
use std::rc::Rc;
use oximo_core::Model;
use oximo_solver::SolverError;
use pounce_rs::IpoptApplication;
use pounce_rs::builder::{Nlp, Problem};
use crate::hybrid::HybridOracle;
use crate::options::{PounceOptionValue, PounceOptions};
use crate::tnlp::{self, DerivativeOracle};
use crate::translate::{
Outcome, Prepared, WarmStart, apply_options, map_status, mu_strategy_str, print_level,
};
pub(crate) type Oracle = Rc<RefCell<HybridOracle>>;
#[expect(clippy::unnecessary_wraps)]
pub(crate) fn build(model: &Model) -> Result<Oracle, SolverError> {
Ok(Rc::new(RefCell::new(HybridOracle::new(model))))
}
pub(crate) fn try_reuse(oracle: &Oracle, model: &Model) -> bool {
let mut o = oracle.borrow_mut();
if o.matches(model) {
o.refresh(model);
true
} else {
false
}
}
pub(crate) fn run(
oracle: &Oracle,
prep: &Prepared,
opts: &PounceOptions,
warm: Option<&WarmStart>,
) -> Result<Outcome, SolverError> {
if oracle.borrow().all_closed_form() {
tnlp::run(oracle, prep, opts, warm)
} else {
run_builder(oracle, prep, opts, warm)
}
}
struct OximoProblem {
oracle: Oracle,
sign: f64,
m: usize,
}
impl Problem for OximoProblem {
fn objective(&self, x: &[f64]) -> f64 {
self.sign * self.oracle.borrow_mut().eval_objective(x)
}
fn n_constraints(&self) -> usize {
self.m
}
fn constraints(&self, x: &[f64], out: &mut [f64]) {
self.oracle.borrow_mut().eval_constraints(x, out);
}
fn gradient(&self, x: &[f64], grad: &mut [f64]) -> bool {
let filled = self.oracle.borrow().try_exact_objective_gradient(x, grad);
if filled {
for g in grad.iter_mut() {
*g *= self.sign;
}
}
filled
}
fn jacobian(&self, x: &[f64], jac: &mut [f64]) -> bool {
self.oracle.borrow().try_exact_dense_jacobian(x, jac)
}
}
fn run_builder(
oracle: &Oracle,
prep: &Prepared,
opts: &PounceOptions,
warm: Option<&WarmStart>,
) -> Result<Outcome, SolverError> {
let mut validator = IpoptApplication::new();
validator.initialize().map_err(|e| SolverError::Backend(format!("pounce init: {e:?}")))?;
apply_options(validator.options_mut(), opts, false)?;
let m = oracle.borrow().num_constraints();
let problem = OximoProblem { oracle: Rc::clone(oracle), sign: prep.sign, m };
let x0 = warm.map_or_else(|| prep.x0.clone(), |w| w.x.clone());
let mut nlp = Nlp::new(problem)
.var_bounds(&prep.x_l, &prep.x_u)
.constraint_bounds(&prep.g_l, &prep.g_u)
.x0(&x0)
.option_int("print_level", print_level(opts));
if let Some(tol) = opts.tol {
nlp = nlp.option_num("tol", tol);
}
if let Some(n) = opts.max_iter {
nlp = nlp.option_int("max_iter", i32::try_from(n).unwrap_or(i32::MAX));
}
if let Some(limit) = opts.universal.time_limit {
nlp = nlp.option_num("max_cpu_time", limit.as_secs_f64());
}
if let Some(s) = opts.mu_strategy {
nlp = nlp.option_str("mu_strategy", mu_strategy_str(s));
}
for &(name, v) in opts.num_opts() {
nlp = nlp.option_num(name, v);
}
for &(name, v) in opts.int_opts() {
nlp = nlp.option_int(name, v);
}
for (name, v) in opts.str_opts() {
nlp = nlp.option_str(name, v);
}
for &(name, v) in opts.bool_opts() {
nlp = nlp.option_str(name, if v { "yes" } else { "no" });
}
for (name, value) in &opts.extra {
nlp = match value {
PounceOptionValue::Num(v) => nlp.option_num(name, *v),
PounceOptionValue::Int(v) => nlp.option_int(name, *v),
PounceOptionValue::Str(v) => nlp.option_str(name, v),
PounceOptionValue::Bool(v) => nlp.option_str(name, if *v { "yes" } else { "no" }),
};
}
let sol = nlp.solve();
let termination = map_status(sol.status);
let raw_log =
(opts.universal.verbose == Some(true)).then(|| format!("EXIT: {:?}\n", sol.status));
let warm = sol.success.then(|| WarmStart {
x: sol.x.clone(),
z_l: Vec::new(),
z_u: Vec::new(),
lambda: sol.multipliers.clone(),
});
Ok(Outcome {
termination,
x: sol.x,
lambda: sol.multipliers,
reduced: None,
objective: Some(sol.objective),
iterations: 0,
warm,
raw_log,
})
}