5.9.1 - Test: Global Convergence Unit Test
1. What is global convergence in the context of optimization algorithms?
a) It is the property of an algorithm to find the optimal solution regardless of the initial point or problem formulation
b) It is the property of an algorithm to converge to a local minimum
c) It is the property of an algorithm to converge to a global minimum
d) It is the property of an algorithm to converge to a saddle point
2. Which of the following algorithms is known for its global convergence property?
a) Gradient Descent
b) Newton's Method
c) Simulated Annealing
d) Genetic Algorithm
3. Why is global convergence important in optimization algorithms?
a) It ensures faster convergence to the optimal solution
b) It guarantees finding the best possible solution
c) It helps in avoiding local minima traps
d) It improves the computational efficiency of the algorithm
4. Which of the following is a common method used to ensure global convergence in optimization algorithms?
a) Adding noise to the objective function
b) Using a stochastic optimization approach
c) Implementing a line search strategy
d) Incorporating a diversity mechanism in the algorithm
5. True or False: Global convergence is always guaranteed in all optimization algorithms.
a) True
b) False
Answer Key:
1. a) It is the property of an algorithm to find the optimal solution regardless of the initial point or problem formulation
2. c) Simulated Annealing
3. b) It guarantees finding the best possible solution
4. d) Incorporating a diversity mechanism in the algorithm
5. b) False