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Hub AI
Lazy evaluation AI simulator
(@Lazy evaluation_simulator)
Hub AI
Lazy evaluation AI simulator
(@Lazy evaluation_simulator)
Lazy evaluation
In programming language theory, lazy evaluation, or call-by-need, is an evaluation strategy which delays the evaluation of an expression until its value is needed (non-strict evaluation) and which avoids repeated evaluations (by the use of sharing).
The benefits of lazy evaluation include:
Lazy evaluation is often combined with memoization, as described in Jon Bentley's Writing Efficient Programs. After a function's value is computed for that parameter or set of parameters, the result is stored in a lookup table that is indexed by the values of those parameters; the next time the function is called, the table is consulted to determine whether the result for that combination of parameter values is already available. If so, the stored result is simply returned. If not, the function is evaluated, and another entry is added to the lookup table for reuse.
Lazy evaluation is difficult to combine with imperative features such as exception handling and input/output, because the order of operations becomes indeterminate.
The opposite of lazy evaluation is eager evaluation, sometimes known as strict evaluation. Eager evaluation is the evaluation strategy employed in most[quantify] programming languages.
Lazy evaluation was introduced for lambda calculus by Christopher Wadsworth. For programming languages, it was independently introduced by Peter Henderson and James H. Morris and by Daniel P. Friedman and David S. Wise.
Delayed evaluation is used particularly in functional programming 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. That is, a statement such as x = expression; (i.e. the assignment of the result of an expression to a variable) clearly calls for the expression to be evaluated and the result is placed in x, but what actually is in x is irrelevant until there is a need for its value via a reference to x in some later expression whose evaluation could itself be deferred, though eventually the rapidly growing tree of dependencies would be pruned to produce some symbol rather than another for the outside world to see.
Lazy evaluation is fundamental in big data frameworks such as Apache Spark, where computations on distributed datasets are delayed until results are explicitly needed, allowing for execution optimizations and reduction of unnecessary processing.
Lazy evaluation
In programming language theory, lazy evaluation, or call-by-need, is an evaluation strategy which delays the evaluation of an expression until its value is needed (non-strict evaluation) and which avoids repeated evaluations (by the use of sharing).
The benefits of lazy evaluation include:
Lazy evaluation is often combined with memoization, as described in Jon Bentley's Writing Efficient Programs. After a function's value is computed for that parameter or set of parameters, the result is stored in a lookup table that is indexed by the values of those parameters; the next time the function is called, the table is consulted to determine whether the result for that combination of parameter values is already available. If so, the stored result is simply returned. If not, the function is evaluated, and another entry is added to the lookup table for reuse.
Lazy evaluation is difficult to combine with imperative features such as exception handling and input/output, because the order of operations becomes indeterminate.
The opposite of lazy evaluation is eager evaluation, sometimes known as strict evaluation. Eager evaluation is the evaluation strategy employed in most[quantify] programming languages.
Lazy evaluation was introduced for lambda calculus by Christopher Wadsworth. For programming languages, it was independently introduced by Peter Henderson and James H. Morris and by Daniel P. Friedman and David S. Wise.
Delayed evaluation is used particularly in functional programming 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. That is, a statement such as x = expression; (i.e. the assignment of the result of an expression to a variable) clearly calls for the expression to be evaluated and the result is placed in x, but what actually is in x is irrelevant until there is a need for its value via a reference to x in some later expression whose evaluation could itself be deferred, though eventually the rapidly growing tree of dependencies would be pruned to produce some symbol rather than another for the outside world to see.
Lazy evaluation is fundamental in big data frameworks such as Apache Spark, where computations on distributed datasets are delayed until results are explicitly needed, allowing for execution optimizations and reduction of unnecessary processing.