Lack of support for higher dimensions

Oct 13, 2025 by magicalceylontours.com 29 views

Introduction

CVXPY is a popular Python library for convex optimization. It provides a high-level interface for formulating and solving convex optimization problems. One of the key features of CVXPY is its ability to handle higher-dimensional variables. However, when it comes to stacking variables to higher dimensions, users are currently forced to rely on NumPy's np.stack function, which can be limiting and may not always work as expected.

The Problem

In CVXPY, variables can be created with shapes of arbitrary dimensions. However, when it comes to stacking variables to higher dimensions, users are currently forced to use NumPy's np.stack function. This can be problematic for several reasons:

Limited functionality: NumPy's np.stack function is designed for stacking arrays, not variables. It may not always work as expected when dealing with CVXPY variables.
Inconsistent behavior: When using NumPy's np.stack function with CVXPY variables, the resulting array may not be a CVXPY variable, which can lead to inconsistent behavior and errors.
Lack of support for higher dimensions: While CVXPY supports higher-dimensional variables, it does not provide a built-in way to stack variables to higher dimensions.

The Solution

To address this issue, we propose adding a cp.stack function to CVXPY. This function would allow users to stack CVXPY variables to higher dimensions in a consistent and efficient manner.

Example Use Case

Here is an example of how the cp.stack function could be used:

# Create two 2D variables (shape 3x4)
x1 = cp.Variable((3, 4))
x2 = cp.Variable((3, 4))

# Use cp.stack to combine them into a 3D array
stacked_x = cp.stack([x1, x2], axis=0)

# Define the constraints and objective
constraints = [x1 <= 0.5, x2 <= 0.5]
objective = cp.Maximize(cp.sum(stacked_x))

# Formulate the problem and solve
problem = cp.Problem(objective, constraints)
problem.solve()

print("\nOptimal value for cp.stack:", problem.value)
print("Optimal solution for stacked_x:", stacked_x)

Alternatives

One possible alternative to the cp.stack function is to use loops over dimensions to stack variables. However, this approach can be cumbersome and may not always work as expected.

Additional Context

This feature request is related to issue #198 on the CVXPY GitHub repository.

Benefits

The cp.stack function would provide several benefits, including:

Improved consistency: The cp.stack function would provide a consistent and efficient way to stack CVXPY variables to higher dimensions.
Increased flexibility: The cp.stack function would allow users to stack variables to higher dimensions in a flexible and efficient manner.
Reduced errors: The cp.stack function would reduce the likelihood of errors caused by inconsistent behavior and incorrect usage of NumPy's np.stack function.

Implementation

To implement the cp.stack function, we would need to modify the CVXPY code to support stacking variables to higher dimensions. This would involve adding a new function to the CVXPY library that takes a list of variables and an axis as input and returns a new variable with the stacked dimensions.

Conclusion

Q: What is the current limitation of CVXPY when it comes to stacking variables to higher dimensions?

A: Currently, CVXPY users are forced to rely on NumPy's np.stack function to stack variables to higher dimensions. However, this can be problematic as NumPy's np.stack function is designed for stacking arrays, not variables. It may not always work as expected when dealing with CVXPY variables.

Q: What are the benefits of adding a `cp.stack` function to CVXPY?

A: The cp.stack function would provide several benefits, including:

Improved consistency: The cp.stack function would provide a consistent and efficient way to stack CVXPY variables to higher dimensions.
Increased flexibility: The cp.stack function would allow users to stack variables to higher dimensions in a flexible and efficient manner.
Reduced errors: The cp.stack function would reduce the likelihood of errors caused by inconsistent behavior and incorrect usage of NumPy's np.stack function.

Q: How would the `cp.stack` function work?

A: The cp.stack function would take a list of variables and an axis as input and return a new variable with the stacked dimensions. This would allow users to stack variables to higher dimensions in a consistent and efficient manner.

Q: What are some potential use cases for the `cp.stack` function?

A: Some potential use cases for the cp.stack function include:

Stacking multiple variables to create a higher-dimensional variable: This would allow users to create higher-dimensional variables by stacking multiple lower-dimensional variables.
Creating a variable with a specific shape: This would allow users to create a variable with a specific shape by stacking variables with different shapes.

Q: How would the `cp.stack` function be implemented?

A: To implement the cp.stack function, we would need to modify the CVXPY code to support stacking variables to higher dimensions. This would involve adding a new function to the CVXPY library that takes a list of variables and an axis as input and returns a new variable with the stacked dimensions.

Q: What are some potential alternatives to the `cp.stack` function?

A: Some potential alternatives to the cp.stack function include:

Using loops over dimensions to stack variables: This would involve using loops to stack variables to higher dimensions, but this approach can be cumbersome and may not always work as expected.
Using NumPy's np.stack function: This would involve using NumPy's np.stack function to stack variables to higher dimensions, but this can be problematic as NumPy's np.stack function is designed for stacking arrays, not variables.

Q: Why is the `cp.stack` function important for CVXPY users?

A: The cp.stack function is important for CVXPY users because it would provide a consistent and efficient way to stack CVXPY variables to higher dimensions. This would improve the flexibility and usability of CVXPY and reduce the likelihood of errors caused by inconsistent behavior and incorrect usage of NumPy's np.stack function.

Q: How can CVXPY users provide feedback on the `cp.stack` function?

A: CVXPY users can provide feedback on the cp.stack function by submitting issues on the CVXPY GitHub repository or by contacting the CVXPY development team directly.