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Assignment operators modify the value of the object.
Operator name | Syntax | Prototype examples (for class T) | ||
---|---|---|---|---|
Inside class definition | Outside class definition | |||
simple assignment | Yes | T& T::operator =(const T2& b); | ||
addition assignment | Yes | T& T::operator +=(const T2& b); | T& operator +=(T& a, const T2& b); | |
subtraction assignment | Yes | T& T::operator -=(const T2& b); | T& operator -=(T& a, const T2& b); | |
multiplication assignment | Yes | T& T::operator *=(const T2& b); | T& operator *=(T& a, const T2& b); | |
division assignment | Yes | T& T::operator /=(const T2& b); | T& operator /=(T& a, const T2& b); | |
remainder assignment | Yes | T& T::operator %=(const T2& b); | T& operator %=(T& a, const T2& b); | |
bitwise AND assignment | Yes | T& T::operator &=(const T2& b); | T& operator &=(T& a, const T2& b); | |
bitwise OR assignment | Yes | T& T::operator |=(const T2& b); | T& operator |=(T& a, const T2& b); | |
bitwise XOR assignment | Yes | T& T::operator ^=(const T2& b); | T& operator ^=(T& a, const T2& b); | |
bitwise left shift assignment | Yes | T& T::operator <<=(const T2& b); | T& operator <<=(T& a, const T2& b); | |
bitwise right shift assignment | Yes | T& T::operator >>=(const T2& b); | T& operator >>=(T& a, const T2& b); | |
this, and most also return *this so that the user-defined operators can be used in the same manner as the built-ins. However, in a user-defined operator overload, any type can be used as return type (including void). can be any type including . |
Definitions Assignment operator syntax Built-in simple assignment operator Assignment from an expression Assignment from a non-expression initializer clause Built-in compound assignment operator Example Defect reports See also |
Copy assignment replaces the contents of the object a with a copy of the contents of b ( b is not modified). For class types, this is performed in a special member function, described in copy assignment operator .
replaces the contents of the object a with the contents of b, avoiding copying if possible (b may be modified). For class types, this is performed in a special member function, described in . | (since C++11) |
For non-class types, copy and move assignment are indistinguishable and are referred to as direct assignment .
Compound assignment replace the contents of the object a with the result of a binary operation between the previous value of a and the value of b .
The assignment expressions have the form
target-expr new-value | (1) | ||||||||
target-expr op new-value | (2) | ||||||||
target-expr | - | the expression to be assigned to |
op | - | one of *=, /= %=, += -=, <<=, >>=, &=, ^=, |= |
new-value | - | the expression (until C++11) (since C++11) to assign to the target |
If new-value is not an expression, the assignment expression will never match an overloaded compound assignment operator. | (since C++11) |
For the built-in simple assignment, the object referred to by target-expr is modified by replacing its value with the result of new-value . target-expr must be a modifiable lvalue.
The result of a built-in simple assignment is an lvalue of the type of target-expr , referring to target-expr . If target-expr is a bit-field , the result is also a bit-field.
If new-value is an expression, it is implicitly converted to the cv-unqualified type of target-expr . When target-expr is a bit-field that cannot represent the value of the expression, the resulting value of the bit-field is implementation-defined.
If target-expr and new-value identify overlapping objects, the behavior is undefined (unless the overlap is exact and the type is the same).
If the type of target-expr is volatile-qualified, the assignment is deprecated, unless the (possibly parenthesized) assignment expression is a or an . | (since C++20) |
new-value is only allowed not to be an expression in following situations: is of a , and new-value is empty or has only one element. In this case, given an invented variable t declared and initialized as T t = new-value , the meaning of x = new-value is x = t. is of class type. In this case, new-value is passed as the argument to the assignment operator function selected by . <double> z; z = {1, 2}; // meaning z.operator=({1, 2}) z += {1, 2}; // meaning z.operator+=({1, 2}) int a, b; a = b = {1}; // meaning a = b = 1; a = {1} = b; // syntax error | (since C++11) |
In overload resolution against user-defined operators , for every type T , the following function signatures participate in overload resolution:
& operator=(T*&, T*); | ||
volatile & operator=(T*volatile &, T*); | ||
For every enumeration or pointer to member type T , optionally volatile-qualified, the following function signature participates in overload resolution:
operator=(T&, T); | ||
For every pair A1 and A2 , where A1 is an arithmetic type (optionally volatile-qualified) and A2 is a promoted arithmetic type, the following function signature participates in overload resolution:
operator=(A1&, A2); | ||
The behavior of every built-in compound-assignment expression target-expr op = new-value is exactly the same as the behavior of the expression target-expr = target-expr op new-value , except that target-expr is evaluated only once.
The requirements on target-expr and new-value of built-in simple assignment operators also apply. Furthermore:
In overload resolution against user-defined operators , for every pair A1 and A2 , where A1 is an arithmetic type (optionally volatile-qualified) and A2 is a promoted arithmetic type, the following function signatures participate in overload resolution:
operator*=(A1&, A2); | ||
operator/=(A1&, A2); | ||
operator+=(A1&, A2); | ||
operator-=(A1&, A2); | ||
For every pair I1 and I2 , where I1 is an integral type (optionally volatile-qualified) and I2 is a promoted integral type, the following function signatures participate in overload resolution:
operator%=(I1&, I2); | ||
operator<<=(I1&, I2); | ||
operator>>=(I1&, I2); | ||
operator&=(I1&, I2); | ||
operator^=(I1&, I2); | ||
operator|=(I1&, I2); | ||
For every optionally cv-qualified object type T , the following function signatures participate in overload resolution:
& operator+=(T*&, ); | ||
& operator-=(T*&, ); | ||
volatile & operator+=(T*volatile &, ); | ||
volatile & operator-=(T*volatile &, ); | ||
Possible output:
The following behavior-changing defect reports were applied retroactively to previously published C++ standards.
DR | Applied to | Behavior as published | Correct behavior |
---|---|---|---|
C++11 | for assignments to class type objects, the right operand could be an initializer list only when the assignment is defined by a user-defined assignment operator | removed user-defined assignment constraint | |
C++11 | E1 = {E2} was equivalent to E1 = T(E2) ( is the type of ), this introduced a C-style cast | it is equivalent to E1 = T{E2} | |
C++20 | compound assignment operators for volatile -qualified types were inconsistently deprecated | none of them is deprecated | |
C++11 | an assignment from a non-expression initializer clause to a scalar value would perform direct-list-initialization | performs copy-list- initialization instead | |
C++20 | bitwise compound assignment operators for volatile types were deprecated while being useful for some platforms | they are not deprecated |
Operator precedence
Operator overloading
Common operators | ||||||
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a = b | ++a | +a | !a | a == b | a[...] | function call |
a(...) | ||||||
comma | ||||||
a, b | ||||||
conditional | ||||||
a ? b : c | ||||||
Special operators | ||||||
converts one type to another related type |
for Assignment operators |
Teaching excellence & educational innovation, creating assignments.
Here are some general suggestions and questions to consider when creating assignments. There are also many other resources in print and on the web that provide examples of interesting, discipline-specific assignment ideas.
What do you want students to learn in your course? What could they do that would show you that they have learned it? To determine assignments that truly serve your course objectives, it is useful to write out your objectives in this form: I want my students to be able to ____. Use active, measurable verbs as you complete that sentence (e.g., compare theories, discuss ramifications, recommend strategies), and your learning objectives will point you towards suitable assignments.
This is the fun side of assignment design. Consider how to focus students’ thinking in ways that are creative, challenging, and motivating. Think beyond the conventional assignment type! For example, one American historian requires students to write diary entries for a hypothetical Nebraska farmwoman in the 1890s. By specifying that students’ diary entries must demonstrate the breadth of their historical knowledge (e.g., gender, economics, technology, diet, family structure), the instructor gets students to exercise their imaginations while also accomplishing the learning objectives of the course (Walvoord & Anderson, 1989, p. 25).
After creating your assignments, go back to your learning objectives and make sure there is still a good match between what you want students to learn and what you are asking them to do. If you find a mismatch, you will need to adjust either the assignments or the learning objectives. For instance, if your goal is for students to be able to analyze and evaluate texts, but your assignments only ask them to summarize texts, you would need to add an analytical and evaluative dimension to some assignments or rethink your learning objectives.
Students can be misled by assignments that are named inappropriately. For example, if you want students to analyze a product’s strengths and weaknesses but you call the assignment a “product description,” students may focus all their energies on the descriptive, not the critical, elements of the task. Thus, it is important to ensure that the titles of your assignments communicate their intention accurately to students.
Think about how to order your assignments so that they build skills in a logical sequence. Ideally, assignments that require the most synthesis of skills and knowledge should come later in the semester, preceded by smaller assignments that build these skills incrementally. For example, if an instructor’s final assignment is a research project that requires students to evaluate a technological solution to an environmental problem, earlier assignments should reinforce component skills, including the ability to identify and discuss key environmental issues, apply evaluative criteria, and find appropriate research sources.
Consider your intended assignments in relation to the academic calendar and decide how they can be reasonably spaced throughout the semester, taking into account holidays and key campus events. Consider how long it will take students to complete all parts of the assignment (e.g., planning, library research, reading, coordinating groups, writing, integrating the contributions of team members, developing a presentation), and be sure to allow sufficient time between assignments.
Is the workload you have in mind reasonable for your students? Is the grading burden manageable for you? Sometimes there are ways to reduce workload (whether for you or for students) without compromising learning objectives. For example, if a primary objective in assigning a project is for students to identify an interesting engineering problem and do some preliminary research on it, it might be reasonable to require students to submit a project proposal and annotated bibliography rather than a fully developed report. If your learning objectives are clear, you will see where corners can be cut without sacrificing educational quality.
If an assignment is vague, students may interpret it any number of ways – and not necessarily how you intended. Thus, it is critical to clearly and unambiguously identify the task students are to do (e.g., design a website to help high school students locate environmental resources, create an annotated bibliography of readings on apartheid). It can be helpful to differentiate the central task (what students are supposed to produce) from other advice and information you provide in your assignment description.
Different instructors apply different criteria when grading student work, so it’s important that you clearly articulate to students what your criteria are. To do so, think about the best student work you have seen on similar tasks and try to identify the specific characteristics that made it excellent, such as clarity of thought, originality, logical organization, or use of a wide range of sources. Then identify the characteristics of the worst student work you have seen, such as shaky evidence, weak organizational structure, or lack of focus. Identifying these characteristics can help you consciously articulate the criteria you already apply. It is important to communicate these criteria to students, whether in your assignment description or as a separate rubric or scoring guide . Clearly articulated performance criteria can prevent unnecessary confusion about your expectations while also setting a high standard for students to meet.
Students make assumptions about the audience they are addressing in papers and presentations, which influences how they pitch their message. For example, students may assume that, since the instructor is their primary audience, they do not need to define discipline-specific terms or concepts. These assumptions may not match the instructor’s expectations. Thus, it is important on assignments to specify the intended audience http://wac.colostate.edu/intro/pop10e.cfm (e.g., undergraduates with no biology background, a potential funder who does not know engineering).
If students are unclear about the goals or purpose of the assignment, they may make unnecessary mistakes. For example, if students believe an assignment is focused on summarizing research as opposed to evaluating it, they may seriously miscalculate the task and put their energies in the wrong place. The same is true they think the goal of an economics problem set is to find the correct answer, rather than demonstrate a clear chain of economic reasoning. Consequently, it is important to make your objectives for the assignment clear to students.
If you have specific parameters in mind for the assignment (e.g., length, size, formatting, citation conventions) you should be sure to specify them in your assignment description. Otherwise, students may misapply conventions and formats they learned in other courses that are not appropriate for yours.
Here is a set of questions you can ask yourself when creating an assignment.
Adapted from the WAC Clearinghouse at http://wac.colostate.edu/intro/pop10e.cfm .
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Let there be N workers and N jobs. Any worker can be assigned to perform any job, incurring some cost that may vary depending on the work-job assignment. It is required to perform all jobs by assigning exactly one worker to each job and exactly one job to each agent in such a way that the total cost of the assignment is minimized.
Let us explore all approaches for this problem.
We generate n! possible job assignments and for each such assignment, we compute its total cost and return the less expensive assignment. Since the solution is a permutation of the n jobs, its complexity is O(n!).
The optimal assignment can be found using the Hungarian algorithm. The Hungarian algorithm has worst case run-time complexity of O(n^3).
A state space tree is a N-ary tree with property that any path from root to leaf node holds one of many solutions to given problem. We can perform depth-first search on state space tree and but successive moves can take us away from the goal rather than bringing closer. The search of state space tree follows leftmost path from the root regardless of initial state. An answer node may never be found in this approach. We can also perform a Breadth-first search on state space tree. But no matter what the initial state is, the algorithm attempts the same sequence of moves like DFS.
The selection rule for the next node in BFS and DFS is “blind”. i.e. the selection rule does not give any preference to a node that has a very good chance of getting the search to an answer node quickly. The search for an optimal solution can often be speeded by using an “intelligent” ranking function, also called an approximate cost function to avoid searching in sub-trees that do not contain an optimal solution. It is similar to BFS-like search but with one major optimization. Instead of following FIFO order, we choose a live node with least cost. We may not get optimal solution by following node with least promising cost, but it will provide very good chance of getting the search to an answer node quickly.
There are two approaches to calculate the cost function:
In this article, the first approach is followed.
Let’s take below example and try to calculate promising cost when Job 2 is assigned to worker A.
Since Job 2 is assigned to worker A (marked in green), cost becomes 2 and Job 2 and worker A becomes unavailable (marked in red).
Now we assign job 3 to worker B as it has minimum cost from list of unassigned jobs. Cost becomes 2 + 3 = 5 and Job 3 and worker B also becomes unavailable.
Finally, job 1 gets assigned to worker C as it has minimum cost among unassigned jobs and job 4 gets assigned to worker D as it is only Job left. Total cost becomes 2 + 3 + 5 + 4 = 14.
Below diagram shows complete search space diagram showing optimal solution path in green.
Complete Algorithm:
Below is the implementation of the above approach:
Time Complexity: O(M*N). This is because the algorithm uses a double for loop to iterate through the M x N matrix. Auxiliary Space: O(M+N). This is because it uses two arrays of size M and N to track the applicants and jobs.
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Carly schnitzler and the new edition of textgened: continuing experiments.
Carly Schnitzler has published a new edition of TextGenEd: Continuing Experiments .
In it, there are 15 open-access assignments that reinforce the humanity of writing, while experimenting with, challenging, and questioning LLMs (Large Language Models) as part of the writing process. Annette Vee and Schnitzler have an assignment in this edition , one that Schnitzler has taught in her Reintroduction to Writing courses multiple times, inspired by Vauhini Vara’s “Ghosts” essay.
Schnitzler and her co-editors hope that this is a useful resource for teachers of writing at various stages of their careers, institution types, and levels of comfort with AI/LLMs. They encourage others to please adapt these assignments and share widely, as they are helpful!
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