A) Minimize or maximize an objective function B) Solving equations C) Generating random numbers D) Counting prime numbers
A) The initial guess B) Limitation on the possible solutions C) The final result D) The mathematical formula
A) Maximization B) Simplification C) Minimization D) Randomization
A) Guess and check B) Simulated annealing C) Simplex method D) Trial and error
A) The area outside the constraints B) The solution space C) The set of all feasible solutions D) The region with the maximum value
A) A random solution B) An incorrect solution C) A solution with no constraints D) A solution that satisfies all the constraints
A) Evaluates the impact of changes in parameters on the solution B) Finds the global optimum C) Generates random solutions D) Selects the best algorithm
A) A constraint function B) Function to be optimized or minimized C) An equation without variables D) A random mathematical operation
A) Algorithmic design B) Quantitative analysis C) Mathematical programming D) Function maximization
A) Four: combinatorial, stochastic, dynamic, and robust optimization B) Two: discrete optimization and continuous optimization C) Three: linear, nonlinear, and integer programming D) One: general optimization
A) Discrete optimization B) Nonlinear programming C) Linear programming D) Continuous optimization
A) Continuous optimization B) Discrete optimization C) Combinatorial optimization D) Integer programming
A) Discrete mathematics B) Linear programming C) Global optimization D) Local optimization
A) 5 B) 4 C) 3 D) 1
A) x = ∞ B) x = -1 C) x = 1 D) x = 0
A) No, it is unbounded B) Yes, it is -infinity C) Yes, it is infinity D) Yes, it is 2
A) Leonid Kantorovich B) John von Neumann C) George B. Dantzig D) Fermat
A) 1947 B) 1950 C) 1960 D) 1939
A) Binary variables. B) Semidefinite matrices. C) Continuous variables. D) Discrete variables.
A) Simplifies the problem B) Adds complexity C) Eliminates trade-offs D) Reduces the number of solutions
A) Pareto optimal B) Inferior C) Non-efficient D) Suboptimal
A) The optimization algorithm B) The designer of the system C) The decision maker D) An external evaluator
A) By interactive sessions with the decision maker B) Automatically by the algorithm C) By ignoring less important objectives D) Through historical data analysis
A) Multi-modal optimization B) Global optimization C) The existence problem D) The feasibility problem
A) Second-order conditions B) The Karush–Kuhn–Tucker conditions C) Feasibility conditions D) First-order conditions
A) Trust regions. B) Line searches. C) Interior-point methods. D) Lagrangian relaxation.
A) Lagrangian relaxation. B) Trust regions. C) Line searches. D) Positive-negative momentum estimation.
A) Simultaneous perturbation stochastic approximation (SPSA) B) Quantum optimization algorithms C) Ellipsoid method D) Interior point methods
A) Quasi-Newton methods B) Simultaneous perturbation stochastic approximation C) Gradient descent D) Coordinate descent methods
A) Electrical engineering. B) Cosmology and astrophysics. C) Microeconomics. D) Engineering, especially aerospace engineering.
A) Civil engineering B) Molecular modeling C) Control engineering D) Operations research |