Cardinal Optimizer (COPT)

https://guide.coap.online/copt/en-doc/
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Cardinal Optimizer (COPT) is a high-performance optimization solver designed to model and solve a...
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### Test Python interface installation (Windows)

Source: https://guide.coap.online/copt/en-doc/pythoninterface.html

Execute a sample Python script to verify that the COPT Python interface has been installed correctly. This script is located in the COPT examples directory.

```bash
python lp_ex1.py

```

--------------------------------

### Install coptpy via COPT package (Windows)

Source: https://guide.coap.online/copt/en-doc/pythoninterface.html

Install the Python interface by running setup.py from the COPT installation directory. Administrator privileges may be required if installed on the system disk.

```bash
python setup.py install

```

--------------------------------

### Install coptpy via COPT package (Linux)

Source: https://guide.coap.online/copt/en-doc/pythoninterface.html

Install the Python interface by running setup.py from the COPT installation directory. Use sudo for installation. If using Anaconda, specify the Anaconda Python executable.

```bash
sudo python setup.py install

```

```bash
sudo /opt/anaconda3/bin/python setup.py install

```

--------------------------------

### Linux Systemd Service Installation Steps

Source: https://guide.coap.online/copt/en-doc/floating.html

Commands for installing and managing the copt_flserver service using systemd on Linux.

```bash
sudo systemctl daemon-reload
```

```bash
sudo systemctl start copt_flserver.service
```

```bash
sudo systemctl enable copt_flserver.service
```

```bash
sudo systemctl restart copt_flserver.service
```

```bash
sudo systemctl stop copt_flserver.service
```

```bash
sudo systemctl disable copt_flserver.service
```

```bash
sudo systemctl status copt_flserver.service
```

--------------------------------

### MacOS Launchd Service Installation Steps

Source: https://guide.coap.online/copt/en-doc/floating.html

Commands for installing and managing the copt_flserver service using launchd on MacOS.

```bash
sudo launchctl load -w /Library/LaunchAgents/copt_flserver.plist
```

```bash
sudo launchctl load -w /Library/LaunchDaemons/copt_flserver.plist
```

```bash
sudo launchctl unload -w /Library/LaunchAgents/copt_flserver.plist
```

```bash
sudo launchctl unload -w /Library/LaunchDaemons/copt_flserver.plist
```

```bash
sudo launchctl list solver.copt.flserver
```

--------------------------------

### Obtain License - Example Output

Source: https://guide.coap.online/copt/en-doc/install.html

This is an example of the output you can expect when successfully obtaining COPT license files using a license key.

```bash
copt_licgen -key 19200817f147gd9f60abc791def047fb
  [Info] Cardinal Optimizer   COPT v8.0.5 20240304
  [Info] Use specific key 19200817f147gd9f60abc791def047fb
  [Info] ***** get new COPT license from licensing server *****
  [Info] Write to license.dat
  [Info] Write to license.key
  [Info] Received new license files from server
  [Info] Done !!!
```

--------------------------------

### Linux Cluster Service Configuration Example

Source: https://guide.coap.online/copt/en-doc/cluster.html

An example of a configured systemd service file for the COPT cluster, with paths updated to a specific installation directory.

```ini
[Unit]
Description=COPT Compute Cluster Server

[Service]
WorkingDirectory=/home/eleven/copt_remote80/cluster
ExecStart=/home/eleven/copt_remote80/cluster/copt_cluster
Restart=always
RestartSec=1

[Install]
WantedBy=multi-user.target

```

--------------------------------

### Start Floating Service

Source: https://guide.coap.online/copt/en-doc/floating.html

Starts the COPT floating token service using systemd.

```bash
sudo systemctl start copt_flserver.service

```

--------------------------------

### Full Python LP Example

Source: https://guide.coap.online/copt/en-doc/pythoninterface.html

This comprehensive example demonstrates defining a linear programming problem, setting its objective and constraints, solving it, and analyzing the results using the COPT Python API.

```python
#
# This file is part of the Cardinal Optimizer, all rights reserved.
#

"""
The problem to solve:

  Maximize:
    1.2 x + 1.8 y + 2.1 z

  Subject to:
    1.5 x + 1.2 y + 1.8 z <= 2.6
    0.8 x + 0.6 y + 0.9 z >= 1.2

  where:
    0.1 <= x <= 0.6
    0.2 <= y <= 1.5
    0.3 <= z <= 2.8
"""

import coptpy as cp
from coptpy import COPT

# Create COPT environment
env = cp.Envr()

# Create COPT model
model = env.createModel("lp_ex1")

# Add variables: x, y, z
x = model.addVar(lb=0.1, ub=0.6, name="x")
y = model.addVar(lb=0.2, ub=1.5, name="y")
z = model.addVar(lb=0.3, ub=2.8, name="z")

# Add constraints
model.addConstr(1.5*x + 1.2*y + 1.8*z <= 2.6)
model.addConstr(0.8*x + 0.6*y + 0.9*z >= 1.2)

# Set objective function
model.setObjective(1.2*x + 1.8*y + 2.1*z, sense=COPT.MAXIMIZE)

# Set parameter
model.setParam(COPT.Param.TimeLimit, 10.0)

# Solve the model
model.solve()

# Analyze solution
if model.status == COPT.OPTIMAL:
  print("Objective value: {}".format(model.objval))
  allvars = model.getVars()

  print("Variable solution:")
  for var in allvars:
    print(" x[{0}]: {1}".format(var.index, var.x))

  print("Variable basis status:")
  for var in allvars:
    print(" x[{0}]: {1}".format(var.index, var.basis))

  # Write model, solution and modified parameters to file
  model.write("lp_ex1.mps")
  model.write("lp_ex1.bas")
  model.write("lp_ex1.sol")
  model.write("lp_ex1.par")
```

--------------------------------

### Barrier Solver Initialization Info

Source: https://guide.coap.online/copt/en-doc/logging.html

Provides initial numerical information about the Barrier solver setup, including problem characteristics and factorization statistics. This is printed at the start of the barrier solver process.

```text
Starting barrier solver using 64 threads

Problem info:
Dualized in presolve:            No
Range of matrix coefficients:    [4e-01,4e+00]
Range of rhs coefficients:       [8e+01,3e+02]
Range of bound coefficients:     [4e+01,1e+02]
Range of cost coefficients:      [2e-01,2e+00]

Factor info:
Number of free columns:          0
Number of dense columns:         0
Number of matrix entries:        2.800e+01
Number of factor entries:        2.800e+01
Number of factor flops:          1.140e+02

```

--------------------------------

### Start COPT Floating Token Server

Source: https://guide.coap.online/copt/en-doc/floating.html

Execute this command in the console or terminal to start the server. Ensure the floating license is in the same directory as the executable.

```bash
copt_flserver

```

--------------------------------

### Install Pyomo using Pip

Source: https://guide.coap.online/copt/en-doc/pyomointerface.html

Installs Pyomo using the standard pip utility. An alternative to conda installation.

```bash
pip install pyomo

```

--------------------------------

### Test coptampl installation on Windows

Source: https://guide.coap.online/copt/en-doc/amplinterface.html

Execute this command in a Windows command prompt to verify the coptampl installation and PATH configuration.

```bash
coptampl -v

```

--------------------------------

### Successful Service Running Log Example

Source: https://guide.coap.online/copt/en-doc/cluster.html

Example output indicating the COPT compute cluster server is running successfully as a system service.

```json
{
    "LimitLoadToSessionType" = "System";
    "Label" = "solver.copt.cluster";
    "OnDemand" = false;
    "LastExitStatus" = 0;
    "PID" = 16406;
    "Program" = "/Applications/copt_remote80/cluster/copt_cluster";
};
```

--------------------------------

### Write MIP Start File (Python)

Source: https://guide.coap.online/copt/en-doc/mipstart.html

Writes the solving results or existing initial solution to a MIP start file (.mst).

```python
model.writeMst()
```

--------------------------------

### Install COPT Python Package from Source

Source: https://guide.coap.online/copt/en-doc/pythoninterface.html

Install the new version of coptpy from the COPT installation package after manually removing old files.

```bash
python setup.py install
```

--------------------------------

### Start COPT Compute Cluster

Source: https://guide.coap.online/copt/en-doc/cluster.html

Execute this command in the terminal to start the COPT compute cluster. Ensure cluster license files are in the same directory as the executable.

```bash
./copt_cluster

```

--------------------------------

### Pyomo Solver Log Output

Source: https://guide.coap.online/copt/en-doc/pyomointerface.html

Example log output from Pyomo during the solving process, showing stages from environment setup to result processing.

```text
[    0.00] Setting up Pyomo environment
[    0.00] Applying Pyomo preprocessing actions
[    0.00] Creating model
[    0.01] Applying solver
[    0.05] Processing results
    Number of solutions: 1
    Solution Information
      Gap: None
      Status: optimal
      Function Value: 88.19999999999999
    Solver results file: results.yml
[    0.05] Applying Pyomo postprocessing actions
[    0.05] Pyomo Finished
```

--------------------------------

### LP Model File Example

Source: https://guide.coap.online/copt/en-doc/fileformats.html

An example of a model file in LP format, which is more readable and algebraic. It includes objective function, constraints, and variable bounds.

```text
\Generated by Cardinal Operations

Maximize
1.2 x + 1.8 y + 2.1 z
Subject To
1.5 x + 1.2 y + 1.8 z <= 2.6
0.8 x + 0.6 y + 0.9 z >= 1.2
Bounds
0.1 <= x <= 0.6
0.2 <= y <= 1.5
0.3 <= z <= 2.8
END

```

--------------------------------

### Install coptpy from COPT package

Source: https://guide.coap.online/copt/en-doc/pythoninterface.html

Install the coptpy Python interface from the COPT installation package. This requires navigating to the Python library directory within the COPT installation path.

```bash
sudo python setup.py install

```

--------------------------------

### Linux Start Cluster Service

Source: https://guide.coap.online/copt/en-doc/cluster.html

Command to start the newly configured COPT cluster service.

```bash
sudo systemctl start copt_cluster.service

```

--------------------------------

### C Callback Registration Example

Source: https://guide.coap.online/copt/en-doc/callback.html

This example shows how to register a custom callback function in C. A custom struct can be defined and passed as user data (usrdata) for context-specific information.

```c
typedef struct {
    // Custom data fields
    int user_param;
} MyUserData;

MyUserData user_data;
user_data.user_param = 123;

// Register the callback
copt_prob* prob = ...; // Assume prob is initialized
copt_int_t ret = copt_set_callback(prob, my_callback_function, &user_data, COPT_CBCONTEXT_MIPNODE | COPT_CBCONTEXT_MIPSOL);

if (ret != COPT_OK) {
    // Handle error
}
```

--------------------------------

### Get Variable by Index

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Retrieve a variable object from an expression builder using its index. The index starts from 0.

```python
# Retrieve the variable for the term at index 1 from expression builder 'expr'
x = expr.getVar(1)
```

--------------------------------

### Retrieve an AffineCone from AffineConeArray by index

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Gets the AffineCone object at a specific index from an AffineConeArray. Indexing starts at 0.

```python
afcone = afconearr.getCone(1)
```

--------------------------------

### Create COPT Environment and Model

Source: https://guide.coap.online/copt/en-doc/cppinterface.html

This snippet shows the initial setup for a C++ application using the COPT optimizer. It demonstrates how to instantiate a COPT environment and create a model within that environment, which is the first step in defining and solving an optimization problem.

```cpp
Envr env;
Model model = env.CreateModel("lp_ex1");

```

--------------------------------

### Get LMI Expression Variable

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Retrieves the variable associated with a specific index in the LMI expression. Indices start from 0.

```python
mat = expr.getVar(1)
```

--------------------------------

### Python Callback Implementation Example

Source: https://guide.coap.online/copt/en-doc/callback.html

This example demonstrates how to implement a custom callback class in Python by inheriting from CallbackBase and overriding the callback function. It shows how to register the callback with COPT and access context-specific information.

```python
class MyCallback(COPT.CallbackBase):
    def callback(self, ctx, data):
        if ctx == COPT.CBCONTEXT_MIPNODE:
            print("Node callback invoked.")
            # Access node-specific information
            node_obj = self.getRelaxSol()
            # ...
        elif ctx == COPT.CBCONTEXT_MIPSOL:
            print("Solution callback invoked.")
            # Access solution-specific information
            sol_obj = self.getSolution()
            # ...
        return 0

# Create a callback object
my_cb = MyCallback()

# Register the callback for multiple contexts
model.setCallback(my_cb, COPT.CBCONTEXT_MIPNODE | COPT.CBCONTEXT_MIPSOL)
```

--------------------------------

### Get Coefficient by Index

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Retrieve the coefficient of a specific variable term from an expression builder using its index. The index starts from 0.

```python
# Retrieve the coefficient for the term at index 1 from expression builder 'expr'
coeff = expr.getCoeff(1)
```

--------------------------------

### Retrieve Affine Cone Builder by Index

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Gets the affine cone builder at a specific index from the AffineConeBuilderArray. Indexing starts at 0.

```python
# Retrieve the second-order affine cone builder at index 1 in afconebuilderarr
affineConeBuilder = afconebuilderarr.getBuilder(1)
```

--------------------------------

### Compressed Column Storage (CCS) Format Example

Source: https://guide.coap.online/copt/en-doc/capiref.html

Examples demonstrating the Compressed Column Storage (CCS) format for sparse matrices, used by COPT_LoadProb. Two methods are shown: using only colMatBeg, and using both colMatBeg and colMatCnt.

```c
// Compressed column storage using colMatBeg
colMatBeg[5]  = {  0,   1,   3,   5,   6};
colMatIdx[6]  = {  0,   0,   1,   1,   2,   2};
colMatElem[6] = {1.1, 1.2, 2.2, 2.3, 3.3, 3.4};

// Compressed column storage using both colMatBeg and colMatCnt.
// The * in the example represents unused spaces.
colMatBeg[4]  = {  0,   1,   5,   7};
colMatCnt[4]  = {  1,   2,   2,   1};
colMatIdx[6]  = {  0,   0,   1,   1,   2,   *,   *,   2};
colMatElem[6] = {1.1, 1.2, 2.2, 2.3, 3.3,   *,   *, 3.4};

```

--------------------------------

### MPS Model File Example

Source: https://guide.coap.online/copt/en-doc/fileformats.html

An example of a model file in MPS format, commonly used for optimization problems. It includes sections for name, objective sense, rows, columns, RHS, and bounds.

```text
NAME          COPTPROB
OBJSENSE
    MAX
ROWS
N  __OBJ___
L  R0000000
G  R0000001
COLUMNS
    x         __OBJ___  1.2
    x         R0000000  1.5
    x         R0000001  0.80000000000000004
    y         __OBJ___  1.8
    y         R0000000  1.2
    y         R0000001  0.59999999999999998
    z         __OBJ___  2.1000000000000001
    z         R0000000  1.8
    z         R0000001  0.90000000000000002
RHS
    RHS       R0000000  2.6000000000000001
    RHS       R0000001  1.2
BOUNDS
LO BOUND     x         0.10000000000000001
UP BOUND     x         0.59999999999999998
LO BOUND     y         0.20000000000000001
UP BOUND     y         1.5
LO BOUND     z         0.29999999999999999
UP BOUND     z         2.7999999999999998
ENDATA

```

--------------------------------

### Retrieve Variable by Index

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Get a Var Class object for a variable using its index in the coefficient matrix. Indices start from 0.

```python
# Retrive variable with indice of 1.
 x = m.getVar(1)

```

--------------------------------

### Retrieve Variable Name

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Use this method to get the current name of a variable. No specific setup is required beyond having a variable object.

```python
varname = v.getName()
```

--------------------------------

### Retrieve Variable Type

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Use `getType()` to get the current type of a variable. No specific setup is required beyond having a variable object.

```python
# Retrieve the type of variable v
vtype = v.getType()
```

--------------------------------

### Initialize ExpConeArray

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Create an empty ExpConeArray or initialize it with existing constraints.

```python
conearr = ExpConeArray()
conearr = ExpConeArray([conex, coney])
```

--------------------------------

### Get Coefficient Matrix

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Retrieve the entire coefficient matrix of the model as a SciPy sparse CSC matrix. This requires the SciPy package to be installed.

```python
# Get the coefficient matrix
A = model.getA()
```

--------------------------------

### Start COPT Command-Line

Source: https://guide.coap.online/copt/en-doc/cmdline.html

Initiates the COPT command-line interface. Ensure you have a valid license.

```bash
copt_cmd

```

--------------------------------

### List License Files in ~/copt/ (Linux)

Source: https://guide.coap.online/copt/en-doc/install.html

Verify the presence of license files in the 'copt' directory of the user's home directory on Linux. Successful installation is indicated by the output listing 'license.dat' and 'license.key'.

```bash
ls ~/copt/

```

--------------------------------

### Retrieve Indicator Constraint by Index

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Use this method to get a specific indicator constraint object using its numerical index. Indices start from 0.

```python
# Retrive indicator constraint with indice of 0
genx = m.getGenConstr(0)

```

--------------------------------

### Retrieve Nonlinear Constraint by Index

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Use this method to get a specific nonlinear constraint object using its numerical index. Indices start from 0.

```python
# Retrieve nonlinear constraint with index 1
r = m.getNlConstr(1)

```

--------------------------------

### Copy Service Configuration to LaunchAgents

Source: https://guide.coap.online/copt/en-doc/floating.html

Copy the configured `copt_flserver.plist` file to the system's LaunchAgents directory to manage it as a user agent.

```bash
sudo cp copt_flserver.plist /Library/LaunchAgents
```

--------------------------------

### Initialize ExpConeBuilderArray with Builders

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Create an `ExpConeBuilderArray` and populate it with existing `ExpConeBuilder` objects.

```python
ExpConeBuilderarr = ExpConeBuilderArray([conex, coney])
```

--------------------------------

### Retrieve Constant Term of Linear Expression

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Use this method to get the constant part of a linear expression. No specific setup is required beyond having a `LinExpr` object.

```python
constant = expr.getConstant()
```

--------------------------------

### Linux Systemd Service Installation and Management

Source: https://guide.coap.online/copt/en-doc/cluster.html

Commands for managing the COPT cluster service using systemd on Linux. This includes enabling, starting, stopping, and checking the status of the service.

```bash
sudo cp copt_cluster.service /lib/systemd/system/

```

```bash
sudo systemctl daemon-reload

```

```bash
sudo systemctl start copt_cluster.service

```

```bash
sudo systemctl enable copt_cluster.service

```

```bash
sudo systemctl restart copt_cluster.service

```

```bash
sudo systemctl stop copt_cluster.service

```

```bash
sudo systemctl disable copt_cluster.service

```

```bash
sudo systemctl status copt_cluster.service

```

--------------------------------

### List License Files in COPT_LICENSE_DIR (Linux)

Source: https://guide.coap.online/copt/en-doc/install.html

Verify that 'license.dat' and 'license.key' are present in the directory specified by COPT_LICENSE_DIR on Linux. This confirms successful license file placement.

```bash
ls $COPT_LICENSE_DIR/

```

--------------------------------

### COPT Cluster Directory Structure Example (Linux)

Source: https://guide.coap.online/copt/en-doc/cluster.html

Illustrates the expected directory layout for a COPT compute cluster on a Linux system, showing pre-installed and user-added COPT versions and their associated libraries and licenses.

```shell
~/copt_remote80/cluster
│  cls.ini
│  copt_cluster
│  license.dat -> cluster license v8.0.5
│  license.key
│
└─data
    └─copt
        └─6.0.7
        │   libcopt.so
        └─8.0.5
        │   libcopt.so
        └─9.0.0
            libcopt.so
            license.dat -> license v9.0.0
            license.key

```

--------------------------------

### Model::LoadMipStart()

Source: https://guide.coap.online/copt/en-doc/cppapi/model.html

Loads the final initial values of variables to the problem. This is useful for providing a starting point for the solver, potentially improving convergence speed.

```APIDOC
## Model::LoadMipStart()

### Description
Load final initial values of variables to the problem.

### Method
`void LoadMipStart()`
```

--------------------------------

### Set MIP Start (Python)

Source: https://guide.coap.online/copt/en-doc/mipstart.html

Use this method to specify initial solution values for variables. Call loadMipStart() once after all calls to setMipStart().

```python
model.setMipStart(vars, startvals)
```

--------------------------------

### Create COPT Environment and Model in C#

Source: https://guide.coap.online/copt/en-doc/csharpinterface.html

This snippet shows the initial steps for using the COPT C# interface: creating a COPT environment and then creating a model within that environment. This is the foundational setup for any optimization task.

```csharp
      Envr env = new Envr();
      Model model = env.CreateModel("lp_ex1");

```

--------------------------------

### Install Pyomo Extras using Conda

Source: https://guide.coap.online/copt/en-doc/pyomointerface.html

Installs additional Pyomo packages and dependencies from the conda-forge channel. Use after installing Pyomo.

```bash
conda install -c conda-forge pyomo.extras

```

--------------------------------

### Navigate to User Directory

Source: https://guide.coap.online/copt/en-doc/install.html

Use this command to ensure you are in your user's home directory before proceeding with environment variable setup.

```bash
cd ~/

```

--------------------------------

### Install CVXPY via Conda

Source: https://guide.coap.online/copt/en-doc/cvxpyinterface.html

Use this command to install CVXPY if you have Anaconda distribution installed. This is the recommended method for ease of use.

```bash
conda install -c conda-forge cvxpy

```

--------------------------------

### Copy Service Configuration to LaunchDaemons

Source: https://guide.coap.online/copt/en-doc/floating.html

Copy the `copt_flserver.plist` file to the system's LaunchDaemons directory for system-wide service management.

```bash
sudo cp copt_flserver.plist /Library/LaunchDaemons
```

--------------------------------

### Install CVXPY via Pip

Source: https://guide.coap.online/copt/en-doc/cvxpyinterface.html

Use this command to install CVXPY using the standard pip package installer. This is an alternative to using conda.

```bash
pip install cvxpy

```

--------------------------------

### Model.writeBasis()

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Writes the LP basic solution to a basis file.

```APIDOC
## Model.writeBasis()

### Description
Write the LP basic solution to a basis file.

### Method
`writeBasis(filename)`

### Parameters
#### Arguments
- **filename** (string) - The name of the basis file to be written.

### Request Example
```python
# Write the basis file "testlp.bas"
m.writeBasis('testlp.bas')
```
```

--------------------------------

### Install PuLP via Pip

Source: https://guide.coap.online/copt/en-doc/pulpinterface.html

Use this command to install the PuLP library using the pip package manager. Suitable for standard Python installations.

```bash
pip install pulp

```

--------------------------------

### Basic COPT Floating Server Service Configuration (plist)

Source: https://guide.coap.online/copt/en-doc/floating.html

This is a template for the `copt_flserver.plist` file. Update the 'Program' path to the actual location of your floating service executable.

```xml
<?xml version="1.0" encoding="UTF-8"?>
<plist version="1.0">
    <dict>
        <key>Label</key>
        <string>solver.copt.flserver</string>
        <key>Program</key>
        <string>/path/to/service/copt_flserver</string>
        <key>RunAtLoad</key>
        <true/>
        <key>KeepAlive</key>
        <true/>
    </dict>
</plist>
```

--------------------------------

### Verify License Files

Source: https://guide.coap.online/copt/en-doc/install.html

Execute this command to verify that the 'license.dat' and 'license.key' files are valid for the current COPT version. Both files must be present in the current working directory.

```bash
copt_licgen -v
```

--------------------------------

### Bypass MacOS Security Check for Installed Directory

Source: https://guide.coap.online/copt/en-doc/cluster.html

Alternatively, if you encounter errors related to unverified developers after installation on MacOS, use this command as root to bypass the quarantine check for the installed COPT remote directory.

```bash
xattr -dr com.apple.quarantine /Applications/copt_remote80
```

--------------------------------

### Create Optimization Environment

Source: https://guide.coap.online/copt/en-doc/pythoninterface.html

Initialize the COPT optimization environment. This is a prerequisite for creating any optimization models.

```python
# Create COPT environment
env = cp.Envr()
```

--------------------------------

### Rejected MIP Start: Incomplete (MipStartMode not 2)

Source: https://guide.coap.online/copt/en-doc/mipstart.html

Indicates that a partial initial solution was provided, but MipStartMode was not set to 2, causing it to be rejected.

```log
Loading 1 initial MIP solution
Initial MIP solution # 1 was rejected: partial

```

--------------------------------

### Install Pyomo using Conda

Source: https://guide.coap.online/copt/en-doc/pyomointerface.html

Installs Pyomo from the conda-forge channel. Recommended for Anaconda Python distributions.

```bash
conda install -c conda-forge pyomo

```

--------------------------------

### Install coptpy via pip

Source: https://guide.coap.online/copt/en-doc/pythoninterface.html

Use this command to install the coptpy package using pip. This is the recommended method.

```bash
pip install coptpy

```

--------------------------------

### Model.setMipStart (multiple overloads)

Source: https://guide.coap.online/copt/en-doc/javaapi/Model.html

Sets initial values for variables for Mixed Integer Programming (MIP) problems.

```APIDOC
## Model.setMipStart(int count, double[] vals)

### Description
Set initial values for variables of given number, starting from the first one.

### Synopsis
`void setMipStart(
 int count,
 double[] vals)`

### Arguments
* `count`: the number of variables to set.
* `vals`: values of variables.
```

```APIDOC
## Model.setMipStart(Var var, double val)

### Description
Set initial value for the specified variable.

### Synopsis
`void setMipStart(Var var, double val)`

### Arguments
* `var`: an interested variable.
* `val`: initial value of the variable.
```

```APIDOC
## Model.setMipStart(Var[] vars, double[] vals)

### Description
Set initial value for the specified variable.

### Synopsis
`void setMipStart(Var[] vars, double[] vals)`

### Arguments
* `vars`: a list of interested variables.
* `vals`: initial values of the variables.
```

--------------------------------

### Model.LoadMipStart

Source: https://guide.coap.online/copt/en-doc/csapi/model.html

Loads the final initial values of variables into the optimization problem.

```APIDOC
## Model.LoadMipStart

### Description
Load final initial values of variables to the problem.

### Synopsis
`void LoadMipStart()`
```

--------------------------------

### Create EnvrConfig Object

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Instantiate an EnvrConfig object to manage client configurations. No setup is required beyond importing the class.

```python
# Create client configuration
envconfig = EnvrConfig()

```

--------------------------------

### Get COPT Result

Source: https://guide.coap.online/copt/en-doc/cmdline.html

Retrieves the current value of a specific parameter or attribute after solving. Use 'get' to list all available options.

```copt
get TimeLimit

```

```copt
get LpObjval

```

```copt
get LpStatus

```

--------------------------------

### Obtain License using Key File

Source: https://guide.coap.online/copt/en-doc/install.html

If your license key is saved in a file named 'key.txt' in the same directory as 'copt_licgen', use this command to obtain the license files.

```bash
copt_licgen -file key.txt
```

--------------------------------

### Install coptpy via pip

Source: https://guide.coap.online/copt/en-doc/pythoninterface.html

Use this command to install or upgrade the coptpy package using pip. This is the recommended method for Windows and Linux.

```bash
pip install coptpy

```

```bash
pip install --upgrade coptpy

```

--------------------------------

### Create COPT Environment and Model in Java

Source: https://guide.coap.online/copt/en-doc/javainterface.html

Initialize a COPT environment and create a model instance. Each model encapsulates a problem and its associated data.

```java
      Envr env = new Envr();
      Model model = env.createModel("lp_ex1");

```

--------------------------------

### Uncompress GZIP Archive for COPT Installation

Source: https://guide.coap.online/copt/en-doc/install.html

Use this command to uncompress the GZIP-format installer archive on MacOS. This will extract the COPT files into a directory.

```bash
tar -xzf CardinalOptimizer-8.0.5-universal_mac.tar.gz
```

--------------------------------

### Configured Systemd Service File

Source: https://guide.coap.online/copt/en-doc/floating.html

An example of a systemd service configuration file with actual paths updated for the COPT floating service.

```ini
[Unit]
Description=COPT Floating Token Server

[Service]
WorkingDirectory=/home/eleven/copt_remote80/floating
ExecStart=/home/eleven/copt_remote80/floating/copt_flserver
Restart=always
RestartSec=1

[Install]
WantedBy=multi-user.target

```

--------------------------------

### Read MIP Start File (Python)

Source: https://guide.coap.online/copt/en-doc/mipstart.html

Reads variable values from a MIP start file (.mst) to be used as initial solution values.

```python
model.readMst()
```

--------------------------------

### Verify License - Example Output

Source: https://guide.coap.online/copt/en-doc/install.html

This output indicates a successful local validation of your COPT license files, confirming they are valid and showing the expiry date.

```bash
copt_licgen -v
  [Info] Cardinal Optimizer   COPT v8.0.5 20240304
  [Info] Run local validation
  [Info] Read license.dat
  [Info] Read license.key
  [Info] Expiry : Tue 2030-12-31 00:00:00 +0800
  [Info] Local validation result: Succeeded
  [Info] Done !!!
```

--------------------------------

### Get MLinExpr Evaluation Values

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Gets the evaluated values of each linear expression within an MLinExpr object. Returns a NumPy ndarray with the same dimensions.

```python
a = np.random.rand(4)
mx = m.addMVar((4, 3), nameprefix="mx")
mexpr = a @ mx
mc = m.addConstrs(mexpr <= 1.0)
model.solve()
print(mc.getValue())

```

--------------------------------

### COPT_AddMipStart

Source: https://guide.coap.online/copt/en-doc/capiref.html

Adds MIP start information for the problem. This can be used to provide an initial feasible solution to the solver.

```APIDOC
## COPT_AddMipStart

### Description
Add MIP start information for the problem. If `list` is `NULL`, then information of the first `num` columns will be added.
One MIP start information will be added for each call to this function.

### Synopsis
`int COPT_AddMipStart(copt_prob *prob, int num, const int *list, double *colVal)`

### Arguments
- **prob** (copt_prob *) - The COPT problem.
- **num** (int) - Number of variables (columns).
- **list** (const int *) - Index of variables (columns). Can be `NULL`.
- **colVal** (double *) - MIP start information.
```

--------------------------------

### Set Client Configuration

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Configure client settings using the set method. Use predefined constants like COPT.CLIENT_WAITTIME or string keys like "nobanner".

```python
# Set client configuration
envconfig.set(COPT.CLIENT_WAITTIME, 600)
envconfig.set(COPT.CLIENT_CLUSTER, "127.0.0.1")
# Turn off the banner output when creating COPT environment (such as version, etc.)
envconfig.set("nobanner", "1")

```

--------------------------------

### Install PuLP via Conda

Source: https://guide.coap.online/copt/en-doc/pulpinterface.html

Use this command to install the PuLP library using the conda package manager. Recommended for Anaconda Python distributions.

```bash
conda install -c conda-forge pulp

```

--------------------------------

### loadMipStart

Source: https://guide.coap.online/copt/en-doc/javaapi/Model.html

Loads the final initial values of variables into the problem.

```APIDOC
## loadMipStart()

### Description
Load final initial values of variables to the problem.

### Method
(Implicitly a method call, not an HTTP endpoint)

### Parameters
None

### Return
void
```

--------------------------------

### Load Cluster Service

Source: https://guide.coap.online/copt/en-doc/cluster.html

Load the new cluster service configuration into launchd to start it.

```bash
sudo launchctl load -w /Library/LaunchAgents/copt_cluster.plist
```

--------------------------------

### Set Model Parameters

Source: https://guide.coap.online/copt/en-doc/pythoninterface.html

Configure optimization parameters before solving. This example sets a time limit of 10 seconds for the solver.

```python
# Set parameter
model.setParam(COPT.Param.TimeLimit, 10.0)
```

--------------------------------

### Load Floating Service

Source: https://guide.coap.online/copt/en-doc/floating.html

Load the floating service into `launchd` to start it automatically on login and keep it running.

```bash
sudo launchctl load -w /Library/LaunchAgents/copt_flserver.plist
```

--------------------------------

### Solver Environment and Model Overview

Source: https://guide.coap.online/copt/en-doc/logging.html

Logs the COPT version, platform, and problem type. It also includes model fingerprint and hardware details.

```text
Using Cardinal Optimizer v8.0.5 on Windows
Model fingerprint: 2c27ab28
Hardware has 64 cores and 128 threads. Using instruction set X86_AVX512_E1 (14)
Minimizing a MIP problem
```

--------------------------------

### Initialize SOSBuilderArray

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Create an empty SOSBuilderArray or initialize it with existing SOS constraint builders.

```python
# Create an empty SOSBuilderArray object.
sosbuilderarr = SOSBuilderArray()
# Create a SOSBuilderArray object and initialize it with SOS constraint builder sosx and sosy
sosbuilderarr = SOSBuilderArray([sosx, sosy])
```

--------------------------------

### ExpConeBuilderArray Constructor

Source: https://guide.coap.online/copt/en-doc/csapi/expconebuilderarray.html

Initializes a new instance of the ExpConeBuilderArray class.

```APIDOC
## ExpConeBuilderArray()

### Description
Constructor of ExpConeBuilderArray object.

### Synopsis
`ExpConeBuilderArray()`
```

--------------------------------

### CallbackBase.where()

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Get context in callback.

```APIDOC
## where()

### Description
Get context in callback.

### Return value
Returns an integer value.
```

--------------------------------

### getConstrs

Source: https://guide.coap.online/copt/en-doc/javaapi/Model.html

Get all constraints in the model.

```APIDOC
## getConstrs()

### Description
Get all constraints in model.

### Method Signature
`ConstrArray getConstrs()`

### Return
Array object of constraints.
```

--------------------------------

### Display Help Messages for copt_flstool

Source: https://guide.coap.online/copt/en-doc/floating.html

Execute the tool without any command to display its help messages, including available commands and options.

```bash
> ./copt_flstool
  COPT Floating Token Server Managing Tool

  copt_flstool [-s server ip] [-p port] [-x passwd] command <param>

  commands are:   addblackrule <127.0.0.1/20[-user@machine]>
                  addwhiterule <127.0.*\[+user@machine]>
                  getfilters
                  getinfo
                  resetfilters
                  setpasswd <xxx>
                  toggleblackrule <n-th>
                  togglewhiterule <n-th>
                  writefilters

```

--------------------------------

### Expr.GetConstant()

Source: https://guide.coap.online/copt/en-doc/csapi/expr.html

Get constant in expression.

```APIDOC
## Expr.GetConstant()

### Description
Get constant in expression.

### Synopsis
`double GetConstant()`

### Return
Constant in expression.
```

--------------------------------

### COPT_WriteMst

Source: https://guide.coap.online/copt/en-doc/capiref.html

Writes the current solution or existing MIP start information in the problem to a file.

```APIDOC
## COPT_WriteMst

### Description
Write solution or existed MIP start information in problem to file.

### Synopsis
`int COPT_WriteMst(copt_prob *prob, const char *mstfilename)`

### Arguments
- **prob** (copt_prob *) - The COPT problem.
- **mstfilename** (const char *) - The path to the MIP start file.
```

--------------------------------

### MQuadExpr.T

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Gets the transpose of the MQuadExpr object.

```APIDOC
## MQuadExpr.T

### Description
Gets the transpose of the MQuadExpr object. Similar to the class method transpose().

### Return value
Returns the transposed MQuadExpr object.

### Example
```python
mqx = MQuadExpr.zeros((2,3))
print(mqx.T.shape) # shape = (3, 2)
```
```

--------------------------------

### Create an Empty ExpConeBuilder

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Instantiate an ExpConeBuilder object to facilitate the creation of exponential cone constraints.

```python
ExpConeBuilder()
```

--------------------------------

### MQuadExpr.shape

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Gets the shape of the MQuadExpr object.

```APIDOC
## MQuadExpr.shape

### Description
Gets the shape of the MQuadExpr object.

### Return value
Integer tuple.

### Example
```python
print(MQuadExpr.zeros((2,3)).shape) # shape = (2, 3)
```
```

--------------------------------

### Navigate to Floating Service Directory

Source: https://guide.coap.online/copt/en-doc/floating.html

Change directory to the root of the COPT floating service installation.

```bash
cd /home/eleven/copt_remote80/floating
```

--------------------------------

### size()

Source: https://guide.coap.online/copt/en-doc/javaapi/VarArray.html

Get the number of variable objects.

```APIDOC
## size()

### Description
Returns the number of variable objects currently in the array.

### Method
Method

### Endpoint
N/A

### Parameters
None

### Request Example
None

### Response
#### Success Response (200)
- **int** (int) - The number of variable objects.

### Response Example
None
```

--------------------------------

### COPT Script File Example

Source: https://guide.coap.online/copt/en-doc/cmdline.html

A script file containing a sequence of COPT commands to automate tasks like reading a model, setting parameters, solving, and writing solutions.

```copt
1#COPT script-in file
2
3read diet.mps
4set timelimit 10
5opt
6writesol diet
7quit

```

--------------------------------

### ProbBuffer.Size()

Source: https://guide.coap.online/copt/en-doc/csapi/probbuffer.html

Get the size of problem buffer.

```APIDOC
## ProbBuffer.Size()

### Description
Get the size of problem buffer.

### Synopsis
`int Size()`

### Return
size of problem buffer.
```

--------------------------------

### COPT Compute Cluster Startup Log with Local License Error

Source: https://guide.coap.online/copt/en-doc/cluster.html

This log indicates a failure to verify the local cluster license, possibly due to an invalid signature in the public key file.

```log
> ./copt_cluster
  [ Info] start COPT Compute Cluster, COPT v8.0.5 20240304
  [Error] Invalid signature in public key file
  [Error] Fail to verify local license

```

--------------------------------

### Obtain License using License Key

Source: https://guide.coap.online/copt/en-doc/install.html

Use this command to obtain COPT license files by providing your unique license key. Ensure you have an internet connection for this process.

```bash
copt_licgen -key 19200817f147gd9f60abc791def047fb
```

--------------------------------

### Expr.Size()

Source: https://guide.coap.online/copt/en-doc/csapi/expr.html

Get number of terms in expression.

```APIDOC
## Expr.Size()

### Description
Get number of terms in expression.

### Synopsis
`long Size()`

### Return
Number of terms.
```

--------------------------------

### ConeArray.GetCone()

Source: https://guide.coap.online/copt/en-doc/csapi/conearray.html

Get the idx-th cone object.

```APIDOC
## ConeArray.GetCone(int idx)

### Description
Get the idx-th cone object.

### Method
Method

### Endpoint
N/A

### Parameters
#### Path Parameters
- **idx** (int) - Required - index of cone.

### Request Example
None

### Response
#### Success Response (200)
- **cone object** (object) - cone object with index idx.

### Response Example
None
```

--------------------------------

### Model.loadMipStart()

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Loads the currently specified initial values into the model. Previously specified initial values will be cleared after this method is called.

```APIDOC
## Model.loadMipStart()

### Description
Load the currently specified initial values into model.

**Notice:** After calling this method, the previously initial values will be cleared, and users can continue to set initial values for specified variables.
```

--------------------------------

### Import CVXPY

Source: https://guide.coap.online/copt/en-doc/cvxpyinterface.html

Import the CVXPY library with the conventional alias 'cp'. Ensure CVXPY V1.3 or later is installed along with COPT.

```python
import cvxpy as cp

```

--------------------------------

### MQuadExpr.size

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Gets the number of elements in the MQuadExpr object.

```APIDOC
## MQuadExpr.size

### Description
Gets the number of elements in the MQuadExpr object.

### Return value
Integer value.

### Example
```python
mqx = MQuadExpr.zeros((2,3))
print(mqx.size)  # size= 6
```
```

--------------------------------

### COPT_GetAffineConeIdx

Source: https://guide.coap.online/copt/en-doc/capiref.html

Get the index of an affine cone by its name.

```APIDOC
## COPT_GetAffineConeIdx

### Description
Get index of affine cone by name.

### Synopsis
```c
int COPT_GetAffineConeIdx(copt_prob *prob, const char *affConeName, int *p_iAffCone)
```

### Arguments
- `prob` (copt_prob *) - The COPT problem.
- `affConeName` (const char *) - Name of affine cone.
- `p_iAffCone` (int *) - Pointer to index of affine cone.
```

--------------------------------

### ExpConeArray()

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Initializes a new ExpConeArray object, optionally populating it with existing exponential cone constraints.

```APIDOC
## ExpConeArray()

### Description
Create a ExpConeArray Class object.
If parameter `cones` is `None`, then build an empty ExpConeArray Class object, otherwise initialize the newly created ExpConeArray Class object with `cones`.

### Method
ExpConeArray(cones=None)

### Parameters
#### Path Parameters
- **cones** (ExpCone, ExpConeArray, list, dict, tupledict) - Optional - Exponential cone constraint to be added. Optional, `None` by default. It can be ExpCone Class object, ExpConeArray Class object, list, dictionary or tupledict Class object.

### Request Example
```python
# Example of creating an empty ExpConeArray
empty_array = ExpConeArray()

# Example of creating an ExpConeArray with existing cones (assuming 'my_cones' is a valid collection)
# initialized_array = ExpConeArray(cones=my_cones)
```
```

--------------------------------

### COPT_GetIndicatorIdx

Source: https://guide.coap.online/copt/en-doc/capiref.html

Get the index of an indicator constraint by its name.

```APIDOC
## COPT_GetIndicatorIdx

### Description
Get index of indicator constraint by name.

### Synopsis
```c
int COPT_GetIndicatorIdx(copt_prob *prob, const char *indicatorName, int *p_iIndicator)
```

### Arguments
- `prob` (copt_prob *) - The COPT problem.
- `indicatorName` (const char *) - Name of indicator constraint.
- `p_iIndicator` (int *) - Pointer to index of indicator constraint.
```

--------------------------------

### Initialize ConeArray

Source: https://guide.coap.online/copt/en-doc/pyapiref.html

Create an empty ConeArray or initialize it with existing SOC constraints.

```python
conearr = ConeArray()
```

```python
conearr = ConeArray([conex, coney])
```

--------------------------------

### COPT_GetQConstrIdx

Source: https://guide.coap.online/copt/en-doc/capiref.html

Get the index of a quadratic constraint by its name.

```APIDOC
## COPT_GetQConstrIdx

### Description
Get index of quadratic constraint by name.

### Synopsis
```c
int COPT_GetQConstrIdx(copt_prob *prob, const char *qConstrName, int *p_iQConstr)
```

### Arguments
- `prob` (copt_prob *) - The COPT problem.
- `qConstrName` (const char *) - Name of quadratic constraint.
- `p_iQConstr` (int *) - Pointer to index of quadratic constraint.
```

--------------------------------

### Load Cluster Service as Daemon

Source: https://guide.coap.online/copt/en-doc/cluster.html

Load the cluster service configuration into launchd to start it as a system daemon.

```bash
sudo launchctl load -w /Library/LaunchDaemons/copt_cluster.plist
```
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