oximo-core 0.4.0

Core modeling types (Variable, Set, Constraint, Model) for oximo
Documentation

oximo-core

Core modeling types for oximo: Model, Variable, Set, Constraint, Objective, Parameter, IndexedVar, Domain, and ModelKind.

Re-exports oximo-expr types (Expr, ExprArena, ExprId, ExprNode, ParamId, VarId) so downstream code does not need a separate oximo-expr import. End users typically depend on the umbrella oximo crate rather than this one directly.

Usage

[dependencies]
oximo-core = "0.4"

Or via the umbrella crate (recommended for end users):

[dependencies]
oximo = "0.4"

Quick example

use oximo_core::prelude::*;

let m = Model::new("transport");

// Scalar variables
variable!(m, x >= 0.0);
variable!(m, 0.0 <= y <= 10.0);

// Constraints (incl. a two-sided range, kept as one constraint)
constraint!(m, c1, x + 2.0 * y <= 14.0);
constraint!(m, c2, 3.0 * x - y >= 0.0);
constraint!(m, band, 1.0 <= x + y <= 12.0);

// Objective (or `objective!(m, Feasibility)` for a pure feasibility problem)
objective!(m, Max, 3.0 * x + 4.0 * y);

println!("kind = {:?}", m.kind()); // LP

Modeling API

The modeling surface is a set of macros: variable!, constraint!, objective!, sum!, set!, and param!. Each expands to the underlying typed model operations, so there is no runtime cost and full compile-time type/borrow checking is preserved.

Model uses interior mutability (RefCell), so a macro can take &m, register variables/constraints, and the variable!-introduced bindings (x, y, ...) are locals you can use immediately.

let m = Model::new("my_model");
variable!(m, x >= 0.0);        // binds a local `x: Expr<'_>`
constraint!(m, cap, x <= 5.0); // uses x while holding &m

Names are unique per registry. Registering a duplicate variable or constraint name panics.

Accessors

m.num_variables()      // usize
m.num_constraints()    // usize
m.variables()          // Ref<'_, Vec<Variable>>
m.constraints()        // Ref<'_, Vec<Constraint>>
m.arena()              // Ref<'_, ExprArena>
m.kind()               // ModelKind, cached, invalidated on change
m.try_objective()      // Result<Objective, Error>
m.variable_id("x")     // Option<VarId>
m.constraint_id("cap") // Option<ConstraintId>

Fixing and unfixing variables

m.fix_var(var_id, 3.0);         // lb = ub = 3.0
m.unfix_var(var_id, 0.0, 10.0); // restore bounds

Variables

Scalar variables

variable!(m, x);                        // free (-inf, +inf)
variable!(m, x >= 0.0);                 // lower bound only
variable!(m, 0.0 <= x <= 10.0);         // both bounds
variable!(m, b, Bin);                   // binary {0, 1}  (also Binary)
variable!(m, 0.0 <= n <= 100.0, Int);   // general integer  (also Integer)
variable!(m, s <= 10.0, SemiCont(2.0)); // semicontinuous: 0 or in [2, 10]
variable!(m, t <= 5.0, SemiInt(1.0));   // semi-integer: 0 or integer in [1, 5]

// Keyword args:
variable!(m, u, lb = 0.0, ub = 10.0);    // same as `0.0 <= u <= 10.0`
variable!(m, v, lb = 0.0, domain = Int); // keyword domain (or a positional `Int`)
variable!(m, w, initial = 3.0);          // warm start  (scalar only)
variable!(m, p, fix = 5.0);              // fixed to 5.0 (scalar only)

Indexed variables

Creates one scalar variable per key in a Set (or range), named base[key], and binds an IndexedVar.

let i = Set::range(0..5);
variable!(m, 0.0 <= x[k in i] <= 10.0);     // uniform bounds
variable!(m, y[k in i] >= 0.0, Int);        // integer family
variable!(m, z[a in rows, b in cols], Bin); // multi-index (Cartesian product)

// Access by key (panics on missing key):
let expr = x[2];  // single key (usize / "name" / (a, b))
let e2 = z[a, b]; // inside the macros: multi-index sugar == z[(&a, &b)]

// Bounds may reference the index -> lowered to per-key bounds:
variable!(m, lower[k] <= w[k in i] <= upper[k]);

// Filtered family: keep only matching keys (no trivial elements built).
variable!(m, d[(i, j) in rc if i == j] >= 0.0);

Domain

Variant Description
Domain::Real Any real number (default)
Domain::Integer Any integer
Domain::Binary 0 or 1
Domain::SemiContinuous { threshold } 0 or any value >= threshold
Domain::SemiInteger { threshold } 0 or any integer >= threshold

Sets

Set is an ordered finite index set. Three variants:

let i = Set::range(0..5);              // Range: i64 keys 0..5
let j = Set::strings(["a", "b", "c"]); // Strings
let k = Set::product(&i, &j);          // Tuples: (0,"a"), (0,"b"), ...
let k = &i * &j;                       // Same via Mul operator

// From sparse ints:
let s = Set::from_ints([0, 2, 4, 8]);

// Filter:
let evens = i.filter(|k| k.as_i64().unwrap() % 2 == 0);

Constraints

==, <=, and >= are written directly, the macro intercepts the tokens, so these are real constraint operators.

constraint!(m, name, lhs <= rhs);                  // named, also >= and ==
constraint!(m, lhs >= rhs);                        // anonymous (auto-named _c0, _c1, ...)
constraint!(m, band, 1.0 <= e <= 3.0);             // two-sided range -> one constraint (expr bounds -> band_lo/band_hi)
constraint!(m, name = format!("c_{k}"), e == rhs); // computed run-time name

Indexed family over a set

// One constraint per key, auto-named supply[seattle], ...
constraint!(m, supply[p in plants], sum!(x[p, q] for q in markets) <= cap[p]);

// Multi-index family (multi-index access sugar: x[i, j]).
constraint!(m, flow[i in 0..n, j in 0..m], x[i, j] >= 0.0);

// Filtered family: only keys passing the guard.
constraint!(m, diag[(i, j) in rc if i == j], x[i, j] <= 1.0);

Second-order cone constraints

soc_constraint! registers ||terms||_2 <= bound. Every term and the bound must be affine. The model classifies as SOCP/MISOCP.

soc_constraint!(m, cone, [x, y] <= t);                       // named -> SocConstraintId
soc_constraint!(m, [x - y, 2.0 * y] <= t + 1.0);             // anonymous (auto-named _soc0, ...)
soc_constraint!(m, name = format!("c_{k}"), [x] <= t);       // computed run-time name
soc_constraint!(m, risk[i in assets], [s[i] * w[i]] <= cap); // family: risk[key] per key

The method form m.add_soc_constraint("cone", [x, y], t) is equivalent.

Summation

sum!(body for k in domain) reads as sum_{k in domain} body. Nest with extra clauses and filter with a trailing if:

constraint!(m, cap, sum!(weights[i] * x[i] for i in items) <= capacity);
objective!(m, Min, sum!(c[i, j] * x[i, j] for i in rows, j in cols));
let evens = sum!(x[i] for i in items if i % 2 == 0); // filtered

Objectives

objective!(m, Min, cost_expr);
objective!(m, Max, revenue_expr);

Parameters

param!(m, rate = 0.05);     // binds a re-bindable `rate: Expr<'_>`
rate.set_param_value(0.07); // change between solves without rebuilding

Indexed parameters

Mirror indexed variables: one re-bindable scalar parameter per key, bound as an IndexedParam. The right-hand side is evaluated per key and may reference the index.

let items = Set::range(0..3);
param!(m, cost[i in items] = base_cost[i]); // one parameter per key
param!(m, w[(i, j) in rc] = weight(i, j));  // multi-index
param!(m, c[p in plants] = price[p]);       // string-keyed (sparse)

let unit = cost[1];             // index for a param `Expr`
cost[1].set_param_value(9.0);   // re-bind one entry via its handle
m.set_param_idx(&cost, 1, 9.0); // ...or by key on the model
m.param_value_idx(&cost, 1);    // -> Some(9.0)

Model kind

Inferred automatically from variables and expressions, cached and invalidated on change. The decision ladder runs top-down. Any integer/binary variable picks the MI* variant of the row that matches:

Kind (continuous/integer) Conditions
NLP/MINLP Any nonlinear expression (degree > 2, transcendentals, division)
QCP/MIQCP Any quadratic constraint not recognized as a second-order cone
SOCP/MISOCP Second-order cones present (explicit or detected)
QP/MIQP Quadratic objective, linear constraints
LP/MILP Everything linear

License

MIT OR Apache-2.0