四川师范大学
- What are the basic function of the abstract ? （ ）
- In the following cases of LP problems, which ones might happen ? （ ）
- https://image.zhihuishu.com/zhs/doctrans/docx2html/202007/fd6f1e10d08843378a475f8f62ddce62.png
- Assume that the constraint matrix
， in the following matrices which ones can be chosen as a basis matrix ? （ ） - When one using the simplex method to solve an LP problem, if there exist basic variables being zeroes in the optimal simplex tableau, then （ ）
- If the feasible region of a LP problem is nonempty, then （ ）
- Consider the nonlinear programming problem
we can obtain that （ ） - From the simplex tableau
we find that ( ) - Assume
, then （ ） - In the following cases for LP problem and its dual problem, which ones might happen ?（ ）
- https://image.zhihuishu.com/zhs/onlineexam/ueditor/202007/c20116f6d92a4a1ab7d16fb3945518e0.png
- The dual linear programming problem of the linear programming problem
is （ ） . 
- https://image.zhihuishu.com/zhs/doctrans/docx2html/202007/607ab6defa914d1c931dd09da13c4dee.png
- In the nonlinear programming problem, the negative gradient direction is a （ ）
- Which of the following types of words are not often used in the title of scientific papers? （ ）
- A linear programming problem is infeasible if（ ）
- If the optimal value of auxiliary problem is equal to zero, then（ ）
- If x is a basic feasible solution of a standard form LP problem, then（ ）
- The purpose of solving the auxiliary problem of an LP problem is （ ）
- https://image.zhihuishu.com/zhs/doctrans/docx2html/202007/351b6ffab9f24ac2b716cec5c90cb46c.png
- The steepest descent method is （ ）
- When we use the dual simplex method to solve an LP problem, in each iteration, we need（ ）
- What is the last step in a scientific presentation? （ ）
- In the procedure of solving an integer linear programming problem, which cases will not happen? （ ）
- The dual of the dual problem coincides with its primal problem.（ ）
- For a standard form LP problem, if a basic solution do not have negative components, then it is a basic feasible solution.（ ）
- The supremum of finitely many convex functions is convex.（ ）
- The linear combination of finitely many convex functions is convex.（ ）
- If the initial iteration point is close enough to the solution, the sequence obtained by Newton's method converges to the local minimizer .（ ）
- A regular solution of an LP problem is a basic solution but not a feasible solution.（ ）
- For an unconstrained convex optimization problem, the iteration sequence obtained by steepest descent method is globally convergent to a minimizer.（ ）
- A nonempty feasible region of a standard form LP problem might not have vertex.（ ）
- If the feasible region of a nonlinear programming problem is convex, then this problem is a convex programming problem.（ ）
- The local minimizer of an LP problem is a global minimizer.（ ）
- If the basic feasible solution has more than one bases, then it is a degenerate basic feasible solution.（ ）
- The Newton's method is valid only when the first and second derivatives of the cost function exist and the second order derivative is not null.（ ）
- When we use the exterior penalty function method to solve the nonlinear programming problem, the penalty parameter should converge to infinity.（ ）
- If the cost function is convex then a local minimizer is also a global minimizer.（ ）
- In a standard form LP problem, a basic feasible solution might have more than one basis.（ ）
A:The basis for deciding which to choose. B:Summarize the whole text in miniature. C:It is required for publication. D:Media to expand circulation.
答案:Summarize the whole text in miniature.###Media to expand circulation.###The basis for deciding which to choose.
A:Unsolvable. B:Has multiple optimal solutions. C:Unbounded. D:Has unique optimal solution.
答案:A, B, C, D
A:it should be iterated again since the artificial variable x5 is basic variable. B:the solution to the original problem is (0,9/4, 7/4 )T. C:the feasible region of the original problem is an empty set. D:the optimal solution to the auxiliary problem is (0,9/4, 7/4,0,3/4,0 )T.
答案:
A:
B:
C:
D:
答案:
A:The LP problem might have multiple solution. B:The LP problem has unique solution. C:The LP problem is degenerated. D:The current optimal solution is not a vertex of the feasible region.
答案:The LP problem might have multiple solution.###The LP problem is degenerated.
A:the LP problem has optimal solution. B:the feasible region is convex. C:the LP problem is bounded. D:the LP problem has basic feasible solution.
答案:A, B, D
A:This nonlinear programming problem is a convex programming problem. B:The active indexes for
is 1.
C: is a feasible solution.
D:
is a feasible solution but not an optimal solution.答案:https://image.zhihuishu.com/zhs/doctrans/docx2html/202007/155a2a05c96c48129034cde30afdfd9b.png###https://image.zhihuishu.com/zhs/doctrans/docx2html/202007/155a2a05c96c48129034cde30afdfd9b.png###This nonlinear programming problem is a convex programming problem.###https://image.zhihuishu.com/zhs/doctrans/docx2html/202007/7d605d9c73d043e2833800b9986d16ea.png
A:The basic feasible solution in the tableau is (7/4, 9/4,0,0, 3/4)T. B:The optimal value of the related LP problem is 7/2. C:The basic variables are x1, x2, x5 . D:The basic feasible solution in this tableau is optimal.
答案:The basic variables are x1, x2, x5 .###The optimal value of the related LP problem is 7/2.###The basic feasible solution in this tableau is optimal.
A:If A is positive definite, the unconstrained optimization problem defined by
has unique optimal solution.
B: is convex if A is positive semi-definite.
C:If A is positive semi-definite but not positive definite, the unconstrained optimization problem defined by might not have optimal solution.
D:The gradient of is 
.答案:
A:The dual problem is unbounded and the primal problem unsolvable. B:The dual problem is unsolvable and the primal problem unbounded. C:Both the primal LP problem and the dual LP problem have optimal solution. D:Both the primal LP problem and the dual LP problem are unsolvable.
A:the sub-problem is unbounded. B:the sub-problem has integer optimal solution. C:the optimal value of sub-problem is greater than the bound. D:the feasible region of sub-problem is an empty set.
A:
B:
C:
D:
A:If it is bounded, then it achieves its minimum on a vertex of the feasible region. B:It is unbounded if its feasible region is unbounded. C:The feasible region has a vertex when the feasible region is nonempty. D:The feasible region might be unbounded.
A:
B:
C:
D:
A:feasible descent direction. B:descent direction. C:feasible direction. D:infeasible direction.
A:Noun. B:Complete sentences. C:Phrase. D:Gerund.
A:the feasible region is an empty-set. B:the LP problem does not achieve its minimum on the feasible region. C:the feasible region is unbounded. D:the cost function is unbounded on the feasible region.
A:The feasible region of original problem is nonempty. B:The feasible region of the original problem is bounded. C:The original problem is unsolvable. D:The original problem has optimal solutions.
A:its nonbasic variables are zeroes. B:its basic variables are positive. C:the number of its positive components is less than the number of rows of the constraint matrix. D:it has only one basis.
A:to obtain a basic feasible solution of original LP problem. B:to obtain a regular solution of original LP problem. C:to obtain an optimal solution of original LP problem. D:to obtain a basic solution of original LP problem.
A:
B:
C:
D:
A:an effective method for constraint nonlinear programming problem. B:locally convergent. C:to use negative gradient direction as searching direction. D:convergent faster than the other gradient methods.
A:The test numbers are non-positive. B:the right hand side vector is non-positive. C:The test numbers are non-negative. D:the right hand side vector is non-negative.
A:Question and answer. B:Report the main content. C:Acknowledgement. D:Self-introduction.
A:The solution is also a solution to the relaxed problem. B:The solution set is a line segment. C:The solution is unique . D:There are multiple solutions.
A:错 B:对
A:对 B:错
A:对 B:错
A:错 B:对
A:错 B:对
A:对 B:错
A:对 B:错
A:对 B:错
A:错 B:对
A:对 B:错
A:错 B:对
A:错 B:对
A:对 B:错
A:错 B:对
A:错 B:对
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