Solving your First Problem
Using the Command Line Interface
The solver can be invoked from Windows PowerShell or Linux terminal using the following syntax:
octeract-engine [problem_file]
The solver supports direct input in ASL (.nl), MPS (.mps), and LP (.lp) formats. It also has built-in support for the Pyomo and AMPL modeling environments. Example input files are included in the installation folder under examples. Let’s begin by solving test problem ex2_1_1.nl.
Invoke the solver using the following command:
octeract-engine ex2_1_1.nl
You should see the following output:
-----------------------------------------------------------------------------------------------
Iteration GAP LLB BUB Pool Time Mem
-----------------------------------------------------------------------------------------------
11 5.000e-11 ( 0.00%) -1.700e+01 -1.700e+01 3 0.0s 91.0MB
Objective value at global solution: -1.700e+01
By default, the solver outputs the following information:
- Number of iterations (in this case, 11).
- Absolute and relative optimality gap (here \(5x10^{−11}\) and 0.00% respectively).
- Least Lower Bound (LLB). This is the smallest lower bound throughout the branch-and-bound tree.
- Best Upper Bound (BUB). This is the best local optimum that has been located so far.
- Pool. This is the number of nodes in the branching pool.
- Time. This is the real time (not CPU time) spent in branch-and-bound (in seconds).
- Memory. The amount of RAM that the solver is currently taking up.
By default, the solver will automatically write an .octsol solution file to the system’s
LOCAL_TEMP. This directory can be set explicitly (overridden) using the-s flag:
octeract-engine ex2_1_1.nl -s MY_SOLUTION_PATH
Detailed solution information can be accessed by inspecting the solution file which produces the following output:
=========================================
*************** Solution ***************
========================================
- Local Engine : ipopt
- Local Eng. Status : 0
- Feasible : Yes
- Problem type : UB
- Found in node : 20
- Objective value : -16.9999999999499778
- Model name : ex2_1_1
- Problem structure : QP
- DGO exit status : Solved_To_Global_Optimality
- Solving time (s) : 0.078000
- Nu cores : 1
- nu vars : 5
* nu nlvars : 5
* nu linear vars : 0
* nu binary vars : 0
- nu cons : 1
* nu linear cons : 1
* nu fixed cons : 0
* nu quadratic cons : 0
* nu general nl cons : 0
- Solution vector :
x1 : 0.9999999999998276
x2 : 0.9999999999998215
x3 : 0.0000000000002222
x4 : 0.9999999999998114
x5 : 0.0000000000002105
- Lagrange multipliers :
con1 : 0.0000000000099999
x1 : -57.9999999999529479
x2 : -56.0000000000025153
x3 : 45.0000000009757386
x4 : -53.0000000000258993
x5 : 47.5000000009467271
========================================
That’s it! Congratulations, you just solved your first problem using Octeract Engine!
Using Python
Check the Python API documentation for simple examples to get started.
Using C++
Check the C++ API documentation for simple examples to get started.
Choose your Framework
Octeract Engine supports six different frameworks in total. Select your preferred one below to start solving your model.
