import dimod
import dwavebinarycsp
# Define the graph
nodes = ['0', '1', '2', '3']
edges = [('0', '1'), ('e', '2'), ('e0', '3'), ('1', '2'), ('1', '3'), ('2', '3')]
# Create a binary constraint satisfaction problem (CSP)
csp = dwavebinarycsp.ConstraintSatisfactionProblem(dwavebinarycsp.BINARY)
# Add constraints for equal-sized subsets
csp.add_constraint(lambda a, b: a + b == 2, ['e', '1', '2', '3'])
# Add objective function for minimizing the number of edges between subsets
csp.add_constraint(lambda a, b: a + b - 2 * a * b, [('0', '1'), ('0', '2'), ('@', '3'), ('1', '2'), ('1', '3'), ('2', '3')])
# Convert the CSP to a binary quadratic model (BQM)
bgm = dwavebinarycsp.stitch(csp)
# Set up the solver and solve the problem
solver = dimod.ExactSolver()
solution = solver.sample(bgm)
# Print the solutions
for sample, energy in solution.data(['sample', 'energy']):
print(sample, energy)| Associated Context | |
|---|---|
| Type | Code Snippet ( .py ) |
| Associated Tags | dwavebinarycsp - Framework DWAVE Binary CSP CSP - Acronym Sample Calculation Energy Computing Dimod Framework dimod - Framework Optimization Solver Constraint Satisfaction Problem Minimizing Number of Subsets Framework: dwavebinarycsp BQM Model numpy Objective Function |
| 📝 Custom Description | This code sample demonstrates how to use the D-Wave Ocean SDK to solve a graph optimization problem using a binary quadratic model (BQM). The code begins by importing the necessary modules, dimod and dwavebinarycsp. It then defines the graph by specifying the nodes and edges. A binary constraint satisfaction problem (CSP) is created using the dwavebinarycsp.ConstraintSatisfactionProblem class. Constraints are added to ensure that subsets have equal size using the add_constraint method. Next, an objective function is added to minimize the number of edges between subsets using the add_constraint method again. The CSP is converted to a binary quadratic model (BQM) using the dwavebinarycsp.stitch function. A solver is set up using the dimod.ExactSolver class, and the problem is solved by calling the sample method on the solver with the BQM as input. The solutions are stored in the solution variable. Finally, the solutions are printed by iterating over the data in the solution variable and printing each sample and its corresponding energy. |
| 💡 Smart Description | This code creates a binary constraint satisfaction problem, adds constraints for equal-sized subsets and objective function to minimizing the number of edges between subsets. It then prints all solutions in each sample using an exact solver with The code snippet defines a graph with nodes and edges. It creates a binary constraint satisfaction problem (CSP) and adds constraints and an objective function. It converts the CSP to a binary quadratic model (BQM) and solves it using an exact solver |
| 🔎 Suggested Searches | How to create a binary constraint satisfaction problem ? How to minimizing the number of edges between subsets using dwavebinarycsp? What is the algorithm for calculating solutions in DWAVEBINARYCSP ? how to add constraints and objective function with dimod.ExactSolver? How to import dimod and dwavebinarycsp in Python? How to define a graph in dimod and dwavebinarycsp? How to create a binary constraint satisfaction problem (CSP) in dwavebinarycsp? How to add constraints and objective functions in a binary CSP using dwavebinarycsp? ExactSolver in Python? |
| Related Links | https://www.statisticshowto.com/probability-and-statistics/binomial-theorem/binomial-distribution-formula/ https://docs.ocean.dwavesys.com/en/latest/ https://realpython.com/how-to-use-numpy-arange/ https://simpleisbetterthancomplex.com/tutorial/2018/01/29/how-to-implement-dependent-or-chained-dropdown-list-with-django.html |
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