Lazy evaluation

Delayed evaluation is used particularly in functional languages. When using delayed evaluation, an expression is not evaluated as soon as it gets bound to a variable, but when the evaluator is forced to produce the expression's value. Some programming languages delay evaluation of expressions by default, and some others provide functions or special syntax to delay evaluation. In Miranda and Haskell, evaluation of function arguments is delayed by default. In many other languages, evaluation can be delayed by explicitly suspending the computation using special syntax (as with Scheme's "delay" and "force") or, ...

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Lazy evaluation, Lazy evaluation - Delayed evaluation

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Mathematical functions have great strengths in terms of flexibility and analysis. For example, if a function is known to be idempotent, then a call to a function which has its own output as its argument, and which is known to have no side-effects, may be efficiently computed without multiple calls. A function in this sense has zero or more parameters and a single return value. The parameters—or arguments, as they are sometimes called—are the inputs to the function, and the return value is the function's output. The definition of a ...

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Functional programming, Functional programming - Introduction, Functional programming - History, Functional programming - Comparison with imperative programming, Functional programming - Functional programming languages, Functional programming - Higher-order functions, Functional programming - Speed and space considerations, Functional programming - Functional languages

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As of 2003, many of the most popular programming languages, including C and its derivatives, do not have direct support for coroutines within the language or their standard libraries. (This is, in large part, due to the limitations of stack-based subroutine implementation). In situations in which a coroutine would be the natural implementation of a mechanism, but is not available, the typical response is to create a subroutine that uses an ad-hoc assemblage of boolean flags and other state variables to maintain an internal state betwe ...

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Coroutine, Coroutine - Brief comparison and example, Coroutine - Detailed comparison, Coroutine - Common uses of coroutines, Coroutine - Programming languages supporting coroutines, Coroutine - Coroutine alternatives and implementations, Coroutine - Implementations for C, Coroutine - Implementations for Python, Coroutine - Implementations for Ruby

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Suppose that plus is a function taking two arguments x and y and returning x + y. In the ML programming language we would define it as follows: plus = fn(x, y) => x + y and plus(1, 2) returns 3 as we expect. The curried version of plus takes a single argument x and returns a new function which takes a single argument y and returns x + y. In ML we would define it as follows: curried_plus = fn(x) => fn(y) => x + y and now when we call curried_plus(1) we get a new ...

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Currying, Currying - Examples, Currying - Mathematical view

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Optimization can be automated by compilers or performed by programmers. Gains are usually limited for local optimization, and larger for global optimizations. Usually, the most powerful optimization is to find a superior algorithm. Optimizing a whole system is usually done by human beings because the system is too complex for automated optimizers. Grid computing or distributed computing aims to optimize the whole ...

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Optimization computer science, Optimization computer science - Basis, Optimization computer science - Tradeoff, Optimization computer science - Different fields, Optimization computer science - Bottlenecks, Optimization computer science - When to optimize, Optimization computer science - Interpreted languages, Optimization computer science - Automated and manual optimization, Optimization computer science - Time taken for optimization, Optimization computer science - Techniques, Optimization computer science - Quotes, Optimization computer science - Subpages

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A compiler analyzes a computer program for the data dependencies between operations. It does this in order to better optimize the instruction sequences. Normally, the compiled output has the results of these optimizations, but the dependency information itself is not recorded within the compiled binary code. Once the instruction was completed by the execution unit, it's output data would be broadcast (with its tag) to the CAM memory. Any other instructions which was dependent on this particular piece of data (identified by the tag value) would be updated. In thi ...

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Dataflow, Dataflow - Software architecture, Dataflow - Diagrams, Dataflow - Concurrency, Dataflow - Hardware architecture

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Purely functional programs have no side-effects. Since functions do not modify state, no data may be changed by parallel function calls. For this reason, pure functions are always thread-safe, a fact which is exploited by languages that use call-by-future evaluation. Because ordering of side-effects does not have to be preserved in their absence, some languages (such as Haskell) use call-by-need evaluation for pure functions. "Pure" functional programming languages typically enforce referential transparency, which is the notion ...

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Functional programming, Functional programming - History, Functional programming - Higher-order functions, Functional programming - Comparison with imperative programming, Functional programming - Pure functions, Functional programming - Monads, Functional programming - Expansion of functional programming, Functional programming - Speed and space considerations, Functional programming - Functional languages

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Functional languages have long been criticised as resource-hungry, both in terms of CPU resources and memory. This was mainly due to two factors: some early functional languages were implemented with little concern for efficiency non-functional languages achieved speed in part by leaving out features such as bounds checking and garbage collection which are considered by many to be important parts of modern computing fr ...

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Functional programming, Functional programming - History, Functional programming - Higher-order functions, Functional programming - Comparison with imperative programming, Functional programming - Pure functions, Functional programming - Monads, Functional programming - Expansion of functional programming, Functional programming - Speed and space considerations, Functional programming - Functional languages

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In operations research, optimization is the problem of determining the inputs of a function that minimize or maximize its value. Sometimes constraints are imposed on the values that the inputs can take; this problem is known as constrained optimization. In computer programming, optimization usually specifically means to modify code and its compilation settings on a given computer architecture to produce more efficient software. Typical problems have such a large number of possibilities that a ...

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Optimization computer science, Optimization computer science - Basis, Optimization computer science - Tradeoff, Optimization computer science - Different fields, Optimization computer science - Bottlenecks, Optimization computer science - When to optimize, Optimization computer science - Interpreted languages, Optimization computer science - Automated and manual optimization, Optimization computer science - Time taken for optimization, Optimization computer science - Techniques, Optimization computer science - Quotes, Optimization computer science - Subpages

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The first computer-based functional programming language was Information Processing Language (IPL) from the RAND corporation. Another very old functional language is Lisp, though neither the original LISP nor modern Lisps such as Common Lisp are pure-functional. Some Lisp variants include Scheme, Dylan, and Logo (though Logo is an imperitive language). The modern canonical examples are Haskell and members of the ML family including SML and OCaml. Others include Erlang, Clean, and Miranda. A third type of a commonly used functional language is Xslt. Ano ...

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Functional programming, Functional programming - History, Functional programming - Higher-order functions, Functional programming - Comparison with imperative programming, Functional programming - Pure functions, Functional programming - Monads, Functional programming - Expansion of functional programming, Functional programming - Speed and space considerations, Functional programming - Functional languages

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Optimization requires finding a bottleneck: the critical part of the code that is the primary consumer of the needed resource. Improving about 20% of code is often responsible for 80% of the results (see also Pareto principle). The architectural design of a system overwhelmingly affects its performance. The choice of algorithm affects efficiency more than any other item of the design. More complex algorithms and data structures perform well with many items, while simple algorithms are more suitable for small amounts of data — the setup and initialization ...

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Optimization computer science, Optimization computer science - Basis, Optimization computer science - Tradeoff, Optimization computer science - Different fields, Optimization computer science - Bottlenecks, Optimization computer science - When to optimize, Optimization computer science - Interpreted languages, Optimization computer science - Automated and manual optimization, Optimization computer science - Time taken for optimization, Optimization computer science - Techniques, Optimization computer science - Quotes, Optimization computer science - Subpages

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Lazy can refer to: Laziness (sloth).  See also Seven deadly sins Lazy evaluation A song by Deep Purple Other related archivesDeep Purple, Laziness, Lazy evaluation, Seven deadly sins

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Load balancing spreads the load over a large number of servers. Often load balancing is done transparently (i.e., without users noticing it), using a so-called layer 4 router. Caching stores intermediate products of computation to avoid duplicate computations. ...

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Optimization computer science, Optimization computer science - Basis, Optimization computer science - Tradeoff, Optimization computer science - Different fields, Optimization computer science - Bottlenecks, Optimization computer science - When to optimize, Optimization computer science - Interpreted languages, Optimization computer science - Automated and manual optimization, Optimization computer science - Time taken for optimization, Optimization computer science - Techniques, Optimization computer science - Quotes, Optimization computer science - Subpages

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On some occasions, the time taken for optimization in itself may be an issue. In a software project, optimizing code usually does not add a new feature, and worse, it might break existing functionalities. Because optimized code has lesser readability then a straightforward code, optimization may well hurt the maintainability of the program as well. In short, optimization becomes a cost and it is important to be sure that the investment pays off. The optimizer (a program that does optimization) may have to be optimized as well. ...

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Optimization computer science, Optimization computer science - Basis, Optimization computer science - Tradeoff, Optimization computer science - Different fields, Optimization computer science - Bottlenecks, Optimization computer science - When to optimize, Optimization computer science - Interpreted languages, Optimization computer science - Automated and manual optimization, Optimization computer science - Time taken for optimization, Optimization computer science - Techniques, Optimization computer science - Quotes, Optimization computer science - Subpages

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Optimization can reduce readability and add code that is used only to improve the performance. This may complicate programs or systems, making them harder to maintain and debug. Compiler optimization, for example, may introduce odd behavior because of compiler bugs. Because of this, optimization or performance tuning must be done at the end of the development stage. Premature optimization occurs when a programmer lets performance considerations affect how he/she designs a piece of code, before getting the design right. This ...

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Optimization computer science, Optimization computer science - Basis, Optimization computer science - Tradeoff, Optimization computer science - Different fields, Optimization computer science - Bottlenecks, Optimization computer science - When to optimize, Optimization computer science - Interpreted languages, Optimization computer science - Automated and manual optimization, Optimization computer science - Time taken for optimization, Optimization computer science - Techniques, Optimization computer science - Quotes, Optimization computer science - Subpages

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Functional programming can be contrasted with imperative programming. Functional programming appears to be missing several constructs often (though incorrectly) considered essential to an imperative language such as C or Pascal. For example, in strict functional programming, there is no explicit memory allocation and no explicit variable assignment. However, these operations occur automatically when a function is invoked: memory allocation occurs to create space for the parameters and the return value, and assignment occurs to copy the param ...

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Functional programming, Functional programming - History, Functional programming - Higher-order functions, Functional programming - Comparison with imperative programming, Functional programming - Pure functions, Functional programming - Monads, Functional programming - Expansion of functional programming, Functional programming - Speed and space considerations, Functional programming - Functional languages

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Tasks can often be performed more efficiently. For example, consider the following C code snippet to sum all integers from 1 to N: int i, sum = 0; for (i = 1; i <= N; i++) sum += i; printf ("sum: %d\n", sum); This code can (assuming no overflow) be rewritten using a mathematical formula like: int sum = (N * (N+1)) / 2; printf ("sum: %d\n", sum); The term "optimization" usually presumes the system retains the same functionality. However, a significant improvement in performance can often be achieve ...

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Optimization computer science, Optimization computer science - Basis, Optimization computer science - Tradeoff, Optimization computer science - Different fields, Optimization computer science - Bottlenecks, Optimization computer science - When to optimize, Optimization computer science - Interpreted languages, Optimization computer science - Automated and manual optimization, Optimization computer science - Time taken for optimization, Optimization computer science - Techniques, Optimization computer science - Quotes, Optimization computer science - Subpages

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A powerful mechanism sometimes used in functional programming is the notion of higher-order functions. Functions are higher-order when they can take other functions as arguments, and/or return functions as results. (The differential operator in calculus is a common example of a function that maps a function to a function.) Higher-order functions were studied in the lambda calculus theory well before the notion of functional programming existed and are present in the design of a number ...

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Functional programming, Functional programming - Introduction, Functional programming - History, Functional programming - Comparison with imperative programming, Functional programming - Functional programming languages, Functional programming - Higher-order functions, Functional programming - Speed and space considerations, Functional programming - Functional languages

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Functional languages have long been criticised as resource-hungry, both in terms of CPU resources and memory. This was mainly due to two things: some early functional languages were implemented with no concern for efficiency non-functional languages achieved speed at least in part by leaving out features such as bounds checking or garbage collection which are viewed as essential parts of modern computing frameworks, the ...

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Functional programming, Functional programming - Introduction, Functional programming - History, Functional programming - Comparison with imperative programming, Functional programming - Functional programming languages, Functional programming - Higher-order functions, Functional programming - Speed and space considerations, Functional programming - Functional languages

Read more here: » Functional programming: Encyclopedia II - Functional programming - Speed and space considerations

Pure functional programs require no variables and have no side-effects, and are therefore automatically thread-safe. They are also automatically verifiable provided that any recursive cycle eventually stops. Nested functions just pass their results back to the main function. Functional languages commonly make quite sophisticated use of the stack. Functional programming often depends heavily on recursion. The Scheme programming language even requires certain types of recursion ( ...

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Functional programming, Functional programming - Introduction, Functional programming - History, Functional programming - Comparison with imperative programming, Functional programming - Functional programming languages, Functional programming - Higher-order functions, Functional programming - Speed and space considerations, Functional programming - Functional languages

Read more here: » Functional programming: Encyclopedia II - Functional programming - Functional programming languages