Examples#
Basic Usage#
To solve a simple LP problem using cuopt_cli:
1#!/bin/bash
2# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
3# SPDX-License-Identifier: Apache-2.0
4#
5# Basic LP Example using cuopt_cli
6#
7# This example demonstrates how to solve a simple LP problem using cuopt_cli.
8# cuopt_cli is a standalone command-line tool that solves LP/MILP problems
9# from MPS files without requiring a server.
10#
11# Requirements:
12# - cuopt_cli installed and available in PATH
13#
14
15# Create a sample MPS file
16echo "* optimize
17* cost = -0.2 * VAR1 + 0.1 * VAR2
18* subject to
19* 3 * VAR1 + 4 * VAR2 <= 5.4
20* 2.7 * VAR1 + 10.1 * VAR2 <= 4.9
21NAME SAMPLE
22ROWS
23 N COST
24 L ROW1
25 L ROW2
26COLUMNS
27 VAR1 COST -0.2
28 VAR1 ROW1 3.0
29 VAR1 ROW2 2.7
30 VAR2 COST 0.1
31 VAR2 ROW1 4.0
32 VAR2 ROW2 10.1
33RHS
34 RHS1 ROW1 5.4
35 RHS1 ROW2 4.9
36ENDATA" > sample.mps
37
38# Solve using default settings
39cuopt_cli sample.mps
40
41# Clean up
42rm -f sample.mps
This should give you the following output:
Output#
Running file sample.mps
Solving a problem with 2 constraints 2 variables (0 integers) and 4 nonzeros
Objective offset 0.000000 scaling_factor 1.000000
Running concurrent
Dual simplex finished in 0.00 seconds
Iter Primal Obj. Dual Obj. Gap Primal Res. Dual Res. Time
0 +0.00000000e+00 +0.00000000e+00 0.00e+00 0.00e+00 2.00e-01 0.033s
PDLP finished
Concurrent time: 0.036s
Solved with dual simplex
Status: Optimal Objective: -3.60000000e-01 Iterations: 1 Time: 0.036s
Mixed Integer Programming Example#
Here’s an example of solving a Mixed Integer Programming (MIP) problem using the CLI:
1#!/bin/bash
2# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
3# SPDX-License-Identifier: Apache-2.0
4#
5# Basic MILP Example using cuopt_cli
6#
7# This example demonstrates how to solve a Mixed Integer Programming (MIP)
8# problem using cuopt_cli. The main difference from LP is that variables
9# are marked as integers in the MPS file.
10#
11# Requirements:
12# - cuopt_cli installed and available in PATH
13#
14
15# Create MILP problem MPS file
16echo "* Optimal solution -28
17NAME MIP_SAMPLE
18ROWS
19 N OBJ
20 L C1
21 L C2
22 L C3
23COLUMNS
24 MARK0001 'MARKER' 'INTORG'
25 X1 OBJ -7
26 X1 C1 -1
27 X1 C2 5
28 X1 C3 -2
29 X2 OBJ -2
30 X2 C1 2
31 X2 C2 1
32 X2 C3 -2
33 MARK0001 'MARKER' 'INTEND'
34RHS
35 RHS C1 4
36 RHS C2 20
37 RHS C3 -7
38BOUNDS
39 UP BOUND X1 10
40 UP BOUND X2 10
41ENDATA" > mip_sample.mps
42
43# Solve the MIP problem with custom parameters
44cuopt_cli --mip-absolute-gap 0.01 --time-limit 10 mip_sample.mps
45
46# Clean up
47rm -f mip_sample.mps
This should produce output similar to:
Output#
Running file mip_sample.mps
Solving a problem with 3 constraints 2 variables (2 integers) and 6 nonzeros
Objective offset 0.000000 scaling_factor 1.000000
After trivial presolve updated 3 constraints 2 variables
Running presolve!
After trivial presolve updated 3 constraints 2 variables
Solving LP root relaxation
Scaling matrix. Maximum column norm 1.225464e+00
Dual Simplex Phase 1
Dual feasible solution found.
Dual Simplex Phase 2
Iter Objective Primal Infeas Perturb Time
1 -3.04000000e+01 7.57868205e+00 0.00e+00 0.00
Root relaxation solution found in 3 iterations and 0.00s
Root relaxation objective -3.01818182e+01
Strong branching on 2 fractional variables
| Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap | Time
0 1 +inf -3.018182e+01 1 0.0e+00 - 0.00
B 3 1 -2.700000e+01 -2.980000e+01 2 6.7e-01 10.4% 0.00
B&B added a solution to population, solution queue size 0 with objective -27
B 4 0 -2.800000e+01 -2.980000e+01 2 7.5e-01 6.4% 0.00
B&B added a solution to population, solution queue size 1 with objective -28
Explored 4 nodes in 0.00s.
Absolute Gap 0.000000e+00 Objective -2.8000000000000004e+01 Lower Bound -2.8000000000000004e+01
Optimal solution found.
Consuming B&B solutions, solution queue size 2
Solution objective: -28.000000 , relative_mip_gap 0.000000 solution_bound -28.000000 presolve_time 0.227418 total_solve_time 0.000000 max constraint violation 0.000000 max int violation 0.000000 max var bounds violation 0.000000 nodes 4 simplex_iterations 3
Using Solver Parameters#
You can customize the solver behavior using various command line parameters. Here’s a comprehensive example showing different parameter options:
1#!/bin/bash
2# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
3# SPDX-License-Identifier: Apache-2.0
4#
5# Solver Parameters Example using cuopt_cli
6#
7# This example demonstrates how to customize solver behavior using various
8# command line parameters with cuopt_cli.
9#
10# Requirements:
11# - cuopt_cli installed and available in PATH
12#
13
14# Create a sample MPS file for testing different parameters
15echo "* optimize
16* cost = -0.2 * VAR1 + 0.1 * VAR2
17* subject to
18* 3 * VAR1 + 4 * VAR2 <= 5.4
19* 2.7 * VAR1 + 10.1 * VAR2 <= 4.9
20NAME SAMPLE
21ROWS
22 N COST
23 L ROW1
24 L ROW2
25COLUMNS
26 VAR1 COST -0.2
27 VAR1 ROW1 3.0
28 VAR1 ROW2 2.7
29 VAR2 COST 0.1
30 VAR2 ROW1 4.0
31 VAR2 ROW2 10.1
32RHS
33 RHS1 ROW1 5.4
34 RHS1 ROW2 4.9
35ENDATA" > sample.mps
36
37echo "=== Example 1: Set absolute primal tolerance and PDLP solver mode ==="
38cuopt_cli --absolute-primal-tolerance 0.0001 --pdlp-solver-mode 1 sample.mps
39
40echo ""
41echo "=== Example 2: Set time limit and use specific solver method (PDLP only) ==="
42cuopt_cli --time-limit 5 --method 1 sample.mps
43
44echo ""
45echo "=== Example 3: Redirect output to log file and solution file ==="
46cuopt_cli --log-to-console false --log-file sample.log --solution-file sample.sol sample.mps
47echo "Log and solution files created: sample.log, sample.sol"
48
49# Clean up
50rm -f sample.mps sample.log sample.sol