ripopt 0.6.1

A memory-safe interior point optimizer in Rust
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290

//! Large-scale benchmark problems for testing the sparse LDL^T solver.
//!
//! Run with: cargo test --release -- --ignored large_scale --nocapture

#[path = "common/large_scale_problems.rs"]
mod problems;
use problems::*;

use ripopt::{NlpProblem, SolveStatus, SolverOptions};
use std::time::Instant;

fn default_options() -> SolverOptions {
    SolverOptions {
        max_wall_time: 300.0,
        tol: 1e-6,
        print_level: 5,
        ..SolverOptions::default()
    }
}

/// Compute max constraint violation from constraint values and bounds.
fn max_cv(problem: &dyn NlpProblem, g: &[f64]) -> f64 {
    let m = problem.num_constraints();
    if m == 0 {
        return 0.0;
    }
    let mut g_l = vec![0.0; m];
    let mut g_u = vec![0.0; m];
    problem.constraint_bounds(&mut g_l, &mut g_u);
    let mut cv = 0.0_f64;
    for i in 0..m {
        let viol_l = (g_l[i] - g[i]).max(0.0);
        let viol_u = (g[i] - g_u[i]).max(0.0);
        cv = cv.max(viol_l).max(viol_u);
    }
    cv
}

#[test]
#[ignore]
fn large_scale_chained_rosenbrock_5k() {
    let problem = ChainedRosenbrock { n: 5000 };
    let options = default_options();
    eprintln!("\n=== Chained Rosenbrock 5K (n=5000, m=0, KKT=5000) ===");
    eprintln!("Solving...");
    let start = Instant::now();
    let result = ripopt::solve(&problem, &options);
    let elapsed = start.elapsed();

    eprintln!(
        "RESULT: status={:?}, obj={:.6e}, iters={}, time={:.3}s",
        result.status, result.objective, result.iterations, elapsed.as_secs_f64()
    );

    assert!(
        result.status == SolveStatus::Optimal,
        "Expected Optimal/Acceptable, got {:?}",
        result.status
    );
    assert!(
        result.objective < 1e-4,
        "f* should be ~0, got {:.6e}",
        result.objective
    );
}

#[test]
#[ignore]
fn large_scale_bratu_10k() {
    let problem = BratuProblem::new(10000);
    let options = default_options();
    eprintln!("\n=== Bratu BVP 10K (n=10000, m=9998, KKT=19998) ===");
    eprintln!("Solving...");
    let start = Instant::now();
    let result = ripopt::solve(&problem, &options);
    let elapsed = start.elapsed();

    let cv = max_cv(&problem, &result.constraint_values);
    eprintln!(
        "RESULT: status={:?}, obj={:.6e}, cv={:.6e}, iters={}, time={:.3}s",
        result.status, result.objective, cv, result.iterations,
        elapsed.as_secs_f64()
    );

    assert!(
        result.status == SolveStatus::Optimal,
        "Expected Optimal/Acceptable, got {:?}",
        result.status
    );
    assert!(cv < 1e-4, "Constraint violation should be small, got {:.6e}", cv);
}

#[test]
#[ignore]
fn large_scale_optimal_control_20k() {
    let problem = OptimalControl::new(9999);
    let options = default_options();
    eprintln!("\n=== Optimal Control 20K (n=19999, m=10000, KKT=29999) ===");
    eprintln!("Solving...");
    let start = Instant::now();
    let result = ripopt::solve(&problem, &options);
    let elapsed = start.elapsed();

    let cv = max_cv(&problem, &result.constraint_values);
    eprintln!(
        "RESULT: status={:?}, obj={:.6e}, cv={:.6e}, iters={}, time={:.3}s",
        result.status, result.objective, cv, result.iterations,
        elapsed.as_secs_f64()
    );

    assert!(
        result.status == SolveStatus::Optimal,
        "Expected Optimal/Acceptable, got {:?}",
        result.status
    );
    assert!(cv < 1e-4, "Constraint violation should be small, got {:.6e}", cv);
}

#[test]
#[ignore]
fn large_scale_poisson_control_50k() {
    let problem = PoissonControl::new(158);
    let options = default_options();
    eprintln!("\n=== Poisson Control 50K (n=49928, m=24964, KKT=74892) ===");
    eprintln!("Solving...");
    let start = Instant::now();
    let result = ripopt::solve(&problem, &options);
    let elapsed = start.elapsed();

    let cv = max_cv(&problem, &result.constraint_values);
    eprintln!(
        "RESULT: status={:?}, obj={:.6e}, cv={:.6e}, iters={}, time={:.3}s",
        result.status, result.objective, cv, result.iterations,
        elapsed.as_secs_f64()
    );

    assert!(
        result.status == SolveStatus::Optimal,
        "Expected Optimal/Acceptable, got {:?}",
        result.status
    );
    assert!(cv < 1e-3, "Constraint violation should be small, got {:.6e}", cv);
}

#[test]
#[ignore]
fn large_scale_sparse_qp_100k() {
    let problem = SparseQP { n: 50000 };
    let options = default_options();
    eprintln!("\n=== Sparse QP 100K (n=50000, m=50000, KKT=100000) ===");
    eprintln!("Solving...");
    let start = Instant::now();
    let result = ripopt::solve(&problem, &options);
    let elapsed = start.elapsed();

    let f0 = problem.objective(&vec![0.5; 50000]);
    let cv = max_cv(&problem, &result.constraint_values);
    eprintln!(
        "RESULT: status={:?}, obj={:.6e}, cv={:.6e}, iters={}, time={:.3}s, f0={:.6e}",
        result.status, result.objective, cv, result.iterations,
        elapsed.as_secs_f64(), f0
    );

    assert!(
        result.status == SolveStatus::Optimal,
        "Expected Optimal/Acceptable, got {:?}",
        result.status
    );
    assert!(
        result.objective < f0,
        "f* should be less than f(x0)={:.6e}, got {:.6e}",
        f0,
        result.objective
    );
}

// ===========================================================================
// Smaller-scale tests (500, 1000, 2500 variables) for quick validation
// ===========================================================================

#[test]
#[ignore]
fn large_scale_rosenbrock_500() {
    let problem = ChainedRosenbrock { n: 500 };
    let options = default_options();
    eprintln!("\n=== Chained Rosenbrock 500 (n=500, m=0, KKT=500) ===");
    eprintln!("Solving...");
    let start = Instant::now();
    let result = ripopt::solve(&problem, &options);
    let elapsed = start.elapsed();
    eprintln!(
        "RESULT: status={:?}, obj={:.6e}, iters={}, time={:.3}s",
        result.status, result.objective, result.iterations, elapsed.as_secs_f64()
    );
    assert!(
        result.status == SolveStatus::Optimal,
        "got {:?}", result.status
    );
    assert!(result.objective < 1e-4, "f*={:.6e}", result.objective);
}

#[test]
#[ignore]
fn large_scale_bratu_1000() {
    let problem = BratuProblem::new(1000);
    let options = default_options();
    eprintln!("\n=== Bratu BVP 1K (n=1000, m=998, KKT=1998) ===");
    eprintln!("Solving...");
    let start = Instant::now();
    let result = ripopt::solve(&problem, &options);
    let elapsed = start.elapsed();
    let cv = max_cv(&problem, &result.constraint_values);
    eprintln!(
        "RESULT: status={:?}, obj={:.6e}, cv={:.6e}, iters={}, time={:.3}s",
        result.status, result.objective, cv, result.iterations, elapsed.as_secs_f64()
    );
    assert!(
        result.status == SolveStatus::Optimal,
        "got {:?}", result.status
    );
    assert!(cv < 1e-4, "cv={:.6e}", cv);
}

#[test]
#[ignore]
fn large_scale_optimal_control_2500() {
    let problem = OptimalControl::new(1249); // n=2499, m=1250
    let options = default_options();
    eprintln!("\n=== Optimal Control 2.5K (n=2499, m=1250, KKT=3749) ===");
    eprintln!("Solving...");
    let start = Instant::now();
    let result = ripopt::solve(&problem, &options);
    let elapsed = start.elapsed();
    let cv = max_cv(&problem, &result.constraint_values);
    eprintln!(
        "RESULT: status={:?}, obj={:.6e}, cv={:.6e}, iters={}, time={:.3}s",
        result.status, result.objective, cv, result.iterations, elapsed.as_secs_f64()
    );
    assert!(
        result.status == SolveStatus::Optimal,
        "got {:?}", result.status
    );
    assert!(cv < 1e-4, "cv={:.6e}", cv);
}

#[test]
#[ignore]
fn large_scale_poisson_control_2500() {
    let problem = PoissonControl::new(35); // n=2*35²=2450, m=35²=1225, KKT=3675
    let options = default_options();
    eprintln!("\n=== Poisson Control 2.5K (n=2450, m=1225, KKT=3675) ===");
    eprintln!("Solving...");
    let start = Instant::now();
    let result = ripopt::solve(&problem, &options);
    let elapsed = start.elapsed();
    let cv = max_cv(&problem, &result.constraint_values);
    eprintln!(
        "RESULT: status={:?}, obj={:.6e}, cv={:.6e}, iters={}, time={:.3}s",
        result.status, result.objective, cv, result.iterations, elapsed.as_secs_f64()
    );
    assert!(
        result.status == SolveStatus::Optimal,
        "got {:?}", result.status
    );
    assert!(cv < 1e-3, "cv={:.6e}", cv);
}

#[test]
#[ignore]
fn large_scale_sparse_qp_1000() {
    let n = 500;
    let problem = SparseQP { n };
    let options = default_options();
    eprintln!("\n=== Sparse QP 1K (n=500, m=500, KKT=1000) ===");
    eprintln!("Solving...");
    let start = Instant::now();
    let result = ripopt::solve(&problem, &options);
    let elapsed = start.elapsed();
    let f0 = problem.objective(&vec![0.5; n]);
    let cv = max_cv(&problem, &result.constraint_values);
    eprintln!(
        "RESULT: status={:?}, obj={:.6e}, cv={:.6e}, iters={}, time={:.3}s, f0={:.6e}",
        result.status, result.objective, cv, result.iterations, elapsed.as_secs_f64(), f0
    );
    assert!(
        result.status == SolveStatus::Optimal,
        "got {:?}", result.status
    );
    assert!(result.objective < f0, "f*={:.6e} >= f0={:.6e}", result.objective, f0);
}