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How to define a constraint in Ortools to set a limit of distinct values

By : Julian Robert Teakle
Date : August 01 2020, 11:00 AM
I hope this helps you . For each value, create a boolean variable selected_value that is true iff at least one value is assigned to it.
For this you will need 2 sets of constraints:
code :
model.AddBoolOr([var_i_equals_value, ...]).OnlyEnforcedIf(selected_value)
for var_i_equals_value in ...:
    model.AddImplication(var_i_equals_value, selected_value)

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Mysql select distinct but limit the distinct values

By : user3301912
Date : March 29 2020, 07:55 AM
it fixes the issue It sounds like you want the id values for three distinct columnx values. Is this right? Try this:
code :
SELECT id, columnx
FROM table AS t1
    SELECT DISTINCT(columnx) AS columnx
    LIMIT 1
) AS t2 ON (t1.columnx = t2.columnx);

Can I have a constraint on count of distinct values in a column in SQL?

By : Nachiket D
Date : March 29 2020, 07:55 AM
This might help you This cannot be done using a check constraint alone, but there is a way using a materialized view and a check constraint as I demonstrate here on my blog. For your example this would be:
code :
create materialized view emp_dep_mv
build immediate
refresh complete on commit as
select emp_id, count(*) cnt
from relatives
group by emp_id;

alter table emp_dep_mv
add constraint emp_dep_mv_chk
check (cnt <= 3)
alter table employees add num_relatives number(1,0) default 0 not null;

-- Populate for existing data
update employees
set num_relatives = (select count(*) from relatives r
                     where r.emp_id = e.emp_id)
where exists (select * from relatives r
              where r.emp_id = e.emp_id);

alter table employees add constraint emp_relatives_chk
check (num_relatives <= 3);

create trigger relatives_trg
after insert or update or delete on relatives
for each row
   if inserting or updating then
      update employees
      set    num_relatives = num_relatives + 1
      where  emp_id = :new.emp_id;
   end if;
   if deleting or updating then
      update employees
      set    num_relatives = num_relatives - 1
      where  emp_id = :old.emp_id;
   end if;

How do constraint optimization in OrTools with coefficents?

By : user1568571
Date : March 29 2020, 07:55 AM
To fix this issue I want to use ortools to generate all the possible combinations of a simple problem as in the following program. In this case I want x and y to be multiplications of 5 and additionally if start is 7, then the values of the x should be 7, 10, 15, 20, 25 and so on. How can I change following code for this?
code :
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from ortools.sat.python import cp_model

class VarArraySolutionPrinter(cp_model.CpSolverSolutionCallback):
  """Print intermediate solutions."""

  def __init__(self, variables):
    self.__variables = variables
    self.__solution_count = 0

  def NewSolution(self):
    self.__solution_count += 1
    for v in self.__variables:
      print('%s=%i' % (v, self.Value(v)), end=' ')

  def SolutionCount(self):
    return self.__solution_count

def mod_or_start():
  model = cp_model.CpModel()

  start = 7
  end = 20
  x = model.NewIntVar(start, end - 1, 'x')  # 8..19
  y = model.NewIntVar(start, end - 1, 'y')  # 8..19

  x_is_start = model.NewBoolVar('x_is_start')
  y_is_start = model.NewBoolVar('y_is_start')
  x_is_modulo_5 = model.NewBoolVar('x_is_modulo_5')
  y_is_modulo_5 = model.NewBoolVar('y_is_modulo_5')

  model.Add(x == start).OnlyEnforceIf(x_is_start)
  model.Add(y == start).OnlyEnforceIf(y_is_start)

  # Buggy.
  # model.AddModuloEquality(0, x, 5).OnlyEnforceIf(x_is_modulo_5)
  # model.AddModuloEquality(0, y, 5).OnlyEnforceIf(y_is_modulo_5)

  # Workaround until the modulo code is fixed.
  sub_x = model.NewIntVar(start // 5, end // 5, 'sub_x')
  sub_y = model.NewIntVar(start // 5, end // 5, 'sub_y')
  model.Add(x == 5 * sub_x).OnlyEnforceIf(x_is_modulo_5)
  model.Add(y == 5 * sub_y).OnlyEnforceIf(y_is_modulo_5)
  # Remove duplicate solutions
  model.Add(sub_x == start // 5).OnlyEnforceIf(x_is_modulo_5.Not())
  model.Add(sub_y == start // 5).OnlyEnforceIf(y_is_modulo_5.Not())

  # At least one option is true.
  model.AddBoolOr([x_is_start, x_is_modulo_5])
  model.AddBoolOr([y_is_start, y_is_modulo_5])

  # Create a solver and solve.
  solver = cp_model.CpSolver()
  solution_printer = VarArraySolutionPrinter([x, y])
  status = solver.SearchForAllSolutions(model, solution_printer)
  print('Status = %s' % solver.StatusName(status))
  print('Number of solutions found: %i' % solution_printer.SolutionCount())

x=15 y=15 
x=15 y=7 
x=10 y=7 
x=7 y=7 
x=7 y=15 
x=7 y=10 
x=10 y=15 
x=10 y=10 
x=15 y=10 
Number of solutions found: 9

OR Tools Constraint that requires equility of variables [ortools]

By : preet
Date : March 29 2020, 07:55 AM
it helps some times I want create a linear solver model using OR-Tools. I have two numVars and one of my constraints is the equility of these two variables. But I could not find a way to set a constraint that takes a variable. I can do this with gurobi library. Below are the codes for both libraries. Is there a way to pass variables to both sides of a constraint such as xa==xb? in advance.
code :
from ortools.linear_solver import pywraplp
solver = pywraplp.Solver('SolveSimpleSystem', pywraplp.Solver.GLOP_LINEAR_PROGRAMMING)

# Create Variables
xa = solver.NumVar(0, solver.infinity(),'xa')
xb = solver.NumVar(0, solver.infinity(),'xb')

# production constraint
solver.Add(xa*400 + xb*300 >= 25000)
solver.Add(xa*300 + xb*400 >= 27000)
solver.Add(xa*200 + xb*500 >= 30000)

solver.Minimize(xa * 20000 + xb * 25000)

print("Rafinery Solution:")
print("Rafinery A work days:", xa.solution_value())
print("Rafinery B work days:", xb.solution_value())
print("Cost : ", solver.Objective().Value())

why is the ORTOOLS guided local search, that starts with a feasible solution considered constraint programming?

By : user3169559
Date : March 29 2020, 07:55 AM
hope this fix your issue There are two reasons.
1) The initial solution heuristics is a combination of fast LS heuristic search and standard constraint programming search.
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