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Why can't we have all three when relevant? Description + formulas + code? I understand why some algorithms (AKS, RSA) would not make sense if you just spit out

by chime 15y ago

Why can't we have all three when relevant? Description + formulas + code? I understand why some algorithms (AKS, RSA) would not make sense if you just spit out the C code raw but I think it would make the paper much easier to understand if there was some code to go along with the formulas and descriptions, especially if the description is hard to decipher like "analyze the performance of the differencing algorithm on random instances by mapping it to a nonlinear rate equation."

xyzzyz 15y ago
In most (if not all) papers I read there was already pseudocode, which is actually better than real programming language, because it lets you express your algorithm in a concise way without all the real world overhead, like initialization, allocation, etc. If you cannot implement algorithm yourself by reading its description and pseudocode, it means that either you don't fully understand what you just read, or your programming skills need some polishing, and you should be rather reading textbooks than research papers for a while. Also keep in mind that papers are written for the colleagues in the field to read, and not for the laymen, so it's no surprise that authors use the terminology familiar to people in the field. It makes no sense to write research papers with laymen in mind, because 99.999% of them don't care and the remaining 0.001% will learn terminology soon enough. Also, you really should not expect that reading research papers will be as easy as reading newspapers, don't you agree?
- KaeseEs 15y ago
  Oftentimes, it is easy to overlook whether that real-world overhead affects the asymptotic performance of your algorithm in either an ideal sense or a pragmatic one. If memory allocation were free or memory were infinite, true constant time rather than amortized constant time push back on dynamic arays/vectors would be possible, etc. Also, initialization often affects correctness, so it's much less frequently skipped even in pseudocode for an algorithm.

by chime 15y ago

xyzzyz 15y ago

In most (if not all) papers I read there was already pseudocode, which is actually better than real programming language, because it lets you express your algorithm in a concise way without all the real world overhead, like initialization, allocation, etc. If you cannot implement algorithm yourself by reading its description and pseudocode, it means that either you don't fully understand what you just read, or your programming skills need some polishing, and you should be rather reading textbooks than research papers for a while. Also keep in mind that papers are written for the colleagues in the field to read, and not for the laymen, so it's no surprise that authors use the terminology familiar to people in the field. It makes no sense to write research papers with laymen in mind, because 99.999% of them don't care and the remaining 0.001% will learn terminology soon enough. Also, you really should not expect that reading research papers will be as easy as reading newspapers, don't you agree?

KaeseEs 15y ago
Oftentimes, it is easy to overlook whether that real-world overhead affects the asymptotic performance of your algorithm in either an ideal sense or a pragmatic one. If memory allocation were free or memory were infinite, true constant time rather than amortized constant time push back on dynamic arays/vectors would be possible, etc. Also, initialization often affects correctness, so it's much less frequently skipped even in pseudocode for an algorithm.