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import sys
import pickle
from pathlib import Path

import torch
import numpy as np

from tqdm import tqdm

DATA_DIR = Path("data")

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import sys
import pickle
from pathlib import Path

import torch
import numpy as np

from tqdm import tqdm

DATA_DIR = Path("data")

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import torch
import torch.nn as nn
from skorch import NeuralNetClassifier


num_classes = 5
input_width = 10


class Module(nn.Module):

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Max Flow/Min Cut Using CPLEX in Python

This program uses the Python cplex library to solve a max flow/min cut problem. More information on max flow/min cut is available in the Wikipedia article.
The basic idea is to assign flow to each edge such that no edge's flow exceeds that edge's capacity.

Be aware that a linear program may not always be the most efficient technique for solving max flow/min cut. This gist is intended more as a demonstration of how to use CPLEX in Python.

The graph I used is:

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Vertex Cover Using CPLEX in Python

This program uses the Python cplex library to solve a vertex cover problem. More information on Vertex Cover is available in the Wikipedia article. The basic idea of the problem is to select the smallest set of vertices such that each edge in the graph is incident on at least one selected vertex. Solving vertex cover is NP-Hard.

For this example, I used the toy graph below.

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