fiber-dsl | A kotlin DSL extension for https

It's a micro-optimization. For code clarity you might prefer the same exception for both, but when you're in a hot loop you'll want to avoid an unnecessary operation. ArrayList being an old class, the effect this has may have varied between times and JDK versions. If someone has enough interest they could benchmark it with 1.8 and newer JDKs to see how much of an optimization it is for get().

Since accessing a negative array index will fail anyway, there is no need to check for it. However the size of the ArrayList is not always the same as the size of its internal array, so it needs to be checked explicitly.

As to why rangeCheckForAdd does check for negative indexes, good question. Adding is slow anyway, so the micro-optimization wouldn't make much of a difference. Maybe they wanted consistent error messaging here.

The actual term you’re asking for is short-circuiting

Further, some operations are deemed short-circuiting operations. An intermediate operation is short-circuiting if, when presented with infinite input, it may produce a finite stream as a result. A terminal operation is short-circuiting if, when presented with infinite input, it may terminate in finite time. Having a short-circuiting operation in the pipeline is a necessary, but not sufficient, condition for the processing of an infinite stream to terminate normally in finite time.

The term “lazy” only applies to intermediate operations and means that they only perform work when being requested by the terminal operation. This is always the case, so when you don’t chain a terminal operation, no intermediate operation will ever process any element.

Finding out whether a terminal operation is short-circuiting, is rather easy. Go to the Stream API documentation and check whether the particular terminal operation’s documentation contains the sentence

This is a short-circuiting terminal operation.

allMatch has it, reduce has not.

This does not mean that such optimizations based on logic or algebra are impossible. But the responsibility lies at the JVM’s optimizer which might do the same for loops. However, this requires inlining of all involved methods to be sure that this conditions always applies and there are no side effect which must be retained. This behavioral compatibility implies that even if the processing gets optimized away, a peek(System.out::println) would keep printing all elements as if they were processed. In practice, you should not expect such optimizations, as the Stream implementation code is too complex for the optimizer.

The docs don't explicitly say, but this is easy enough to test.

Here is one way, although I'm not sure if it's the most elegant

First, you dynamically allocate one struct, but not the other. So you're comparing apples to oranges.

Statically-allocated structs:

What is the real meaning or definition of running time and execution time? Are they the same of different?

The definition of "running time" in 'Introduction to Algorithms' by C,L,R,S [CLRS] is actually not a time, but a number of steps. This is not what you would intuitively use as a definition. Most would agree that "runnning" and "executing" are the same concept, and that "time" is expressed in a unit of time (like milliseconds). So while we would normally consider these two terms to have the same meaning, in CLRS they have deviated from that, and gave a different meaning to "running time".

Does running time describe the number of steps executed or not?

It does mean that in CLRS. But the definition that CLRS uses for "running time" is particular, and not the same as you might encounter in other resources.

CLRS assumes here that a primitive operation (i.e. a step) takes O(1) time. This is typically true for CPU instructions, which take up to a fixed maximum number of cycles (where each cycle represents a unit of time), but it may not be true in higher level languages. For instance, some languages have a sort instruction. Counting that as a single "step" would give useless results in an analysis.

Breaking down an algorithm into its O(1) steps does help to analyse the complexity of an algorithm. Counting the steps for different inputs may only give a hint about the complexity though. Ultimately, the complexity of an algorithm requires a (mathematical) proof, based on the loops and the known complexity of the steps used in an algorithm.

Does running time depend on the computer or not?

Certainly the execution time may differ. This is one of the reasons we want to by a new computer once in a while.

The number of steps may depend on the computer. If both support the same programming language, and you count steps in that language, then: yes. But if you would do the counting more thoroughly and would count the CPU instructions that are actually ran by the compiled program, then it might be different. For instance, a C compiler on one computer may generate different machine code than a different C compiler on another computer, and so the number of CPU instructions may be less on the one than the other, even though they result from the same C program code.

Practically however, this counting at CPU instruction level is not relevant for determining the complexity of an algorithm. We generally know the time complexity of each instruction in the higher level language, and that is what counts for determining the overall complexity of an algorithm.

So I seem to have fixed it, although I'm still not sure I understand exactly what I've done...

mapcar is by itself a primitive applicative operator (pag. 220 of Common Lisp: A gentle introduction to Symbolic Computation). So, if you want to rewrite it in an applicative way, you should use some other primitive applicative operator, for instance map or map-into. For instance, with map-into:

What do you expect self.add_label(root) to do? According to your method definition, it takes text as argument, so when you say self.add_label(root), you are passing root as text. And what is root? It is '.', so remove it and it'll be gone.

Though a proper way to do this will be to pass a parent argument to the method and use that while widget creation:

And the important part is, your instantiating the class wrong. Keep a reference to it, rather than creating a lot of instances.