solveminmax is a Python module that allows you to solve a sum of min/max equations
by taking advantage of the powerful sympy library.
For instance, you can solve min(400, 500x) + min(200, 500x) + min(0, 500x) = 700
with the assumption that x is within range (0, 1).
Documentation: https://hegelim.github.io/solveminmax/
Legacy Note:
From v0.1.0, the former module, solve-sum-minmax was renamed to solveminmax
so that it follows the Python naming conventions.
Quick Start:
Let's say you want to solve the equation
min(500, 600a) + max(400, 500a) = 500. However, solving it in Math means you
would have to set up the conditions, then solve the check for each one of them,
which sounds like a lot of work, especially for smart people like you
who know how to take advantage of existing tools. So you ask yourself,
"What if there is a library that lets me solve it like a piece of cake?" Well,
there is a library for you now! First off, you need to install it via pip
in your terminal like below:
pip install solveminmax
Or, you can directly clone the project in your local repo and install it using setup.py like below:
git clone https://github.com/Hegelim/solveminmax.git
python setup.py install
Then to solve your problem, simply type in these codes in your Python console:
>>> from solveminmax import solver
>>> eq = "min(500, 600*a) + max(400, 500*a) = 500"
>>> solver.auto_solve(eq, "a")
FiniteSet(1/6)
Whola! In fact, this is a pretty complex problem, but
you just solved it with 3 lines of code. But hold on, what does it mean?
Let's break it down: the core function
auto_solve takes in two required parameters
equation and var_name. equation takes in a string of the equation you want to solve
and var_name lets you define your variable with flexibility, such as "a"
or "x", although currently, it only supports "a".
You can also pass in "low", "high", which lets you specify the range
of your variable. Further details are included in the docstring
if you are interested.
Perks:
- Fast: the module solves a set of complex min and max equations usually under 100 ms, depending on your hardware. For example, for an equation as complex as below, it takes 7 ms on average to give you a solution:
>>> from solveminmax import solver
>>> eq = "max(600*a, 400) + min(200*a, 500) + min(100, 300*a) + 50*a = 600"
>>> %timeit solver.auto_solve(eq, "a")
FiniteSet(4/11)
7.1 ms ± 225 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
-
Accurate: it handles exact Rational numbers and intervals.
-
Flexible：you have a lot of flexibility in defining your equation, see below.
-
Versatile: start from
v0.1.4, you can plot your equation and check your results visually. An example is:
>>> from solveminmax import plot
>>> eq = "-2*min(300, 400*a) = -600"
>>> plot.make_plot(eq, "a")
Version history:
| Version | Core Ideas | Return Rationals | Return Intervals | Error Handling | Plotting |
|---|---|---|---|---|---|
| v0.0.1 | numerical methods | No | No | No | No |
| v0.0.2 | numerical methods | No | No | No | No |
| v0.0.3 | combinations | Yes | No | No | No |
| v0.0.4 | intervals | Yes | Yes | No | No |
| v0.1.0 | intervals | Yes | Yes | Yes | No |
| v0.1.4 | intervals | Yes | Yes | Yes | Yes |
For more information, check the documentation.
Contact:
- Email: yz4175@columbia.edu
- Collaboration: collaborations are welcomed, please email me if you are interested.