#!/usr/bin/env python3.7

# Copyright 2023, Gurobi Optimization, LLC

# This example reads an LP model from a file and solves it.
# If the model can be solved, then it finds the smallest positive variable,
# sets its upper bound to zero, and resolves the model two ways:
# first with an advanced start, then without an advanced start
# (i.e. 'from scratch').

import sys
import gurobipy as gp
from gurobipy import GRB

if len(sys.argv) < 2:
    print('Usage: lpmod.py filename')

# Read model and determine whether it is an LP

model = gp.read(sys.argv[1])
if model.IsMIP == 1:
    print('The model is not a linear program')


status = model.Status

if status == GRB.INF_OR_UNBD or status == GRB.INFEASIBLE \
   or status == GRB.UNBOUNDED:
    print('The model cannot be solved because it is infeasible or unbounded')

if status != GRB.OPTIMAL:
    print('Optimization was stopped with status %d' % status)

# Find the smallest variable value
for v in model.getVars():
    if v.X > 0.0001 and v.X < minVal and v.LB == 0.0:
        minVal = v.X
        minVar = v

print('\n*** Setting %s from %g to zero ***\n' % (minVar.VarName, minVal))
minVar.UB = 0.0

# Solve from this starting point

# Save iteration & time info
warmCount = model.IterCount
warmTime = model.Runtime

# Reset the model and resolve
print('\n*** Resetting and solving without an advanced start ***\n')

coldCount = model.IterCount
coldTime = model.Runtime

print('*** Warm start: %g iterations, %g seconds' % (warmCount, warmTime))
print('*** Cold start: %g iterations, %g seconds' % (coldCount, coldTime))

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