Design of Complex Neuroscience Experiments using Mixed Integer Linear Programming

This repository provides example implementations for the four case studies in the article "Design of Complex Experiments using Mixed Integer Linear Programming". Each case study aims to demonstrate how Mixed Integer Linear Programming (MILP) can be used to address real-world experimental design challenges. Full details of each case study can be found in the main article. The article also contains an introduction to the mathematical foundations of MILP.

Code Organization

Each case study is solved by creating mixed integer linear programs. The included milp python package is used to create and solve these programs. This package can be installed using the included setup.py file. A jupyter notebook in the notebooks directory gives a guided walkthrough of each case study and reproduces the figures from the main article.

milp Workflow

Each mixed integer linear program is constructed using the same basic workflow:

1. Initialize Program

milp.program.initialize_program() initializes a dictionary that represents a mixed integer linear program. This dictionary will be updated to include the program's variables, linear constraints, and cost function terms.

import milp
program = milp.program.initialize_program()

2. Add Variables

milp.program.add_variable() adds a variable to a program. The name given to each variable will be used to specify its constraints and cost function coefficients. Whether a varaible is real, integer, or boolean can be specified by setting variable_type to float, int, or bool.

milp.program.add_variable(
    program=program,
    name='a',
    variable_type=bool,
)
milp.program.add_variable(
    program=program,
    name='b',
    variable_type=int,
    lower_bound=float('-inf'),
    upper_bound=float('inf'),
)
milp.program.add_variable(
    program=program,
    name='c',
    variable_type=float,
)

3. Add Linear Constraints

milp.program.add_constraint() adds a linear constraint to program. An equality constraint can be specified by using arguments A_eq and b_eq. The value of A_eq should be a dictionary whose keys are variable names and whose values are coefficients of those variables. Inequality constraints can be specified by using A_lt and b_lt.

milp.program.add_constraint(
    program=program,
    A_eq={'a': 1, 'b': 1},
    b_eq=0,
)
milp.program.add_constraint(
    program=program,
    A_lt={'a': 1, 'b': 2, 'c': 3},
    b_lt=3,
)

4. Specify Cost Function

milp.program.add_cost_terms is used to specify the program's cost function. The value of coefficients should be a dictionary whose keys are variable names and whose values are coefficients of those variables.

milp.program.add_cost_terms(
    program=program,
    coefficients={'a': -1, 'b': 1},
)

5. Solve Program

milp.program.solve_program() solves the program using an external library (by default uses Gurobi). It does this by 1) converting the program into the library-compatible representation, 2) running its solver, and 3) returning the solution. This solution specifies an experimental design that optimally conforms to the design constraints of the program.

solution = milp.program.solve_MILP(program=program)
print(solution['variables'])

{'a': True, 'b': -1, 'c': 0.0}

Taken together these code snippets have represented and solved the following simple program:

Some additional functions are used to implement the design patterns discussed in Section 3.2 of the main article. For further details refer to the source code and docstrings of milp.

Software Licensing

• All code in this repository, including the code in the milp package and the code in this Jupyter notebook, is licensed under a BSD 2-clause license.
• By default the milp package solves programs using the external library Gurobi. Gurobi offers free academic licenses, available here. Instructions for installing Gurobi can be found on the Gurobi website.
• Alternative solvers can be used instead of Gurobi. The milp also contains an adapter for CPLEX, used by specifying solve_MILP(..., solver='cplex').