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# Nonlinear Programming Final Exam

## section A

Indicate whether each statement is true (+) or false (o). If false, briefly state why or give a
counterexample.

_____ 1. A barrier function adds a penalty to the objective when the solution approaches a
boundary of the feasible region.

_____ 2. The number of "dependent" variables of the GRG algorithm is equal to the number of
equality constraints.

_____ 3. The cubic interpolation procedure for one-dimensional minimization requires the
computation of second derivatives.

_____ 4. The function xa is convex for x>0.

_____ 5. The feasible directions algorithm computes a search direction by solving an LP .

_____ 6. The Fibonacci method for one -dimensional minimization requires the computation of
derivatives.

_____ 7. If all posynomials in a (posynomial) GP problem are condensed into single terms,
then it is always possible to rewrite the resulting problem as an LP problem.

_____ 8. A linear function is neither convex nor concave.

_____ 9. The Gradient Projection method always follows the boundary of the feasible region,
and never enters the interior.

_____ 10. The logarithm of the dual GP objective v(δ,λ) above is a concave function of δ and λ.

_____ 11. The logarithm of the dual GP objective v(δ,λ) above is not a concave function of
both δ and λ, but becomes so if you eliminate using _____ 12. A locally optimal solution for a signomial geometric programming problem must be
globally optimal.

_____ 13. The dual ("pseudo-dual") of a signomial GP problem may have multiple local
maxima, but its global maximum will have the same objective value as the global
minimum of the primal signomial GP.

_____ 14. Penalty functions may be used for both equality and inequality constraints , but barrier
functions may be used only for inequality constraints.

_____ 16. If the Lagrangian function L(x,λ) has a saddlepoint , then there is zero duality
gap between primal and dual optimal solutions.

_____ 17. Consider the problem: min f(x) subject to gi(x) ≤ 0, i=1,2...m, x ≥ 0 (where gi satisfy
some constraint qualification) and its Lagrangian function Then if f and gi are convex, the partial derivative of L with respect to each xj must be
zero at the optimum.

_____ 18. For the problem in the previous statement , must be zero at the optimum.

_____ 19. For the problem in the previous statement, either xj or (or possibly both)
must be zero at the optimum.

_____20. The limit, as w → 0, of the function (c/w)w is ∞.

_____21. _____22. If we define the function then _____23. When the dual problem is solved, the primal variables of a posynomial GP problem
are often computed by exponentiating the Lagrange multipliers of the orthogonality
constraints.

_____24. The function f(x,y) = xy is a concave function of x and y.

_____25. If an algorithm is applied to a minimization problem with optimal value zero, and the
objective value is approximately halved at each iteration, then we would say that the
algorithm has a linear rate of convergence.

_____26. The Hessian matrix of (xtQx + cx) is 2Q.

_____27. In the QC/LD problem, the objective function is assumed to be quadratic.

_____28. If fi(x) is linear for i=1,2,...n, then the function F(x) defined by F(x)= max{fi(x):
i=1,2,...n} is a convex function.

_____29. If a posynomial GP objective function continues to decrease as xj → 0 for some primal
variable xj, then the dual problem objective is unbounded.

_____30. If a posynomial GP objective function continues to decrease as xj → +∞ for some
primal variable xj, then the dual problem is infeasible.

_____ 31. Every posynomial function is convex.

_____ 32. The dual of a constrained posynomial GP problem has only linear equality and
nonnegativity constraints.

_____ 33. If a primal GP constraint is slack, then all the weights of the terms in that constraint
must be zero.

_____ 34. In the QC/LD problem, the variables are restricted to be integer-valued.

_____ 35. The gradient of the function f(x) = (xtQx + cx) is Qx + c.

_____ 36. Solving the QC/LD problem requires a one-dimensional search at each iteration.

_____ 37. The posynomial constraint gi(x) ≤1 has a convex feasible region.

_____ 38. It is always possible (e.g., by a change of variable ) to reformulate a posynomial GP
problem so as to have a convex objective and convex feasible region.

_____ 39. The objective of the posynomial GP problem, i.e., is a concave function of δ and λ.

_____ 40. In the "Golden Section Search" method, more than one-third of the interval of
uncertainty is eliminated at each iteration (assuming that no function values are equal).

_____ 41. If all posynomials in a (posynomial) GP problem are condensed into single terms,
then the resulting problem has a feasible region which includes the original feasible
region.

_____ 42. The Hessian matrix of a quadratic function is constant.

_____ 43. In Wolfe's complementary pivoting algorithm for QP, if a single artificial variable is
used, then the tableau does not require an objective row.

_____ 44. The gradient projection method computes Lagrange multipliers at each iteration, and
stops when they have the appropriate sign.

_____ 45. "Quasi-Newton" search methods for unconstrained minimization require the
computation of second partial derivatives.

_____ 46. Newton's method for unconstrained minimization requires the computation of second
partial derivatives.

_____ 47. If you guess at the value of some primal GP variable and then fix it at this value, the
dual GP problem becomes more difficult to solve.

_____ 48. In a "generalized linear programming" problem, the column of coefficients of the
variables
must be selected as well as the variables.

_____ 49. The function eax is convex only for a ≥ 0.

_____ 50. The Davidon-Fletcher-Powell (DFP) algorithm requires the computation of partial
derivatives.

_____ 51 . If a posynomial function is "condensed" into a monomial, the monomial function (if
not equal) is an overestimate of the posynomial function.

_____ 52 . Wolfe's method for quadratic programming requires a one-dimensional search at
every iteration.

_____ 53. If a constrained nonlinear programming problem satisfies a "constraint qualification",
then a point which satisfies the Karush-Kuhn-Tucker conditions must be an optimal
solution.

_____ 54. A barrier function allows a constraint to be violated, but adds a penalty if the
constraint is violated.

_____ 55. The GRG algorithm requires the use of a one-dimensional search method.

_____ 56. The Feasible Directions algorithm requires the use of a one-dimensional search
method.

_____ 57. If a constrained nonlinear programming problem satisfies a "constraint qualification",
then the Karush-Kuhn-Tucker conditions must be satisfied by an optimal solution.

_____ 58. The function eax is convex for all values of a.

_____ 59. In Wolfe's complementary pivoting method for quadratic programming, the
complementary slackness conditions are satisfied after each iteration.

_____ 60 . The tableau for Wolfe's method for quadratic programming includes columns for
both primal and dual variables.

_____ 61. The function xa is convex for a ≥ 0.

_____ 62. If a nonlinear programming problem has only linear constraints, then any point which
satisfies the Karush-Kuhn-Tucker conditions must be optimal.

_____ 63. The Fletcher-Reeves method is also known as a "Quasi-Newton" method.

_____ 64. The quadratic interpolation procedure for one-dimensional minimization requires the
computation of derivatives.

_____ 65. If the GRG algorithm were applied to a LP problem, it would produce, at each
iteration, the same solution as the simplex algorithm for LP.

_____ 66. A penalty function allows a constraint to be violated, but adds a penalty if the
constraint is violated.

_____ 67. The Lagrangian dual of a convex quadratic programming problem is a quadratic
programming problem with only nonnegativity constraints on the dual variables.

_____ 68. Powell's algorithm for unconstrained minimization requires the computation of partial
derivatives.

_____ 69. The Davidon-Fletcher-Powell method is also known as the "Conjugate Gradient"
method.

_____ 70. In the QC/LD problem, the objective function is assumed to be convex.

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