Given a computable sequence of natural numbers, it is a natural task to find a G\"odel number of a program that generates this sequence. It is easy to see that this problem is neither continuous nor computable. In algorithmic learning theory this problem is well studied from several perspectives and one question studied there is for which sequences this problem is at least learnable in the limit. Here we study the problem on all computable sequences and we classify the Weihrauch complexity of it. For this purpose we can, among other methods, utilize the amalgamation technique known from learning theory. As a benchmark for the classification we use closed and compact choice problems and their jumps on natural numbers, and we argue that these problems correspond to induction and boundedness principles, as they are known from the Kirby-Paris hierarchy in reverse mathematics. We provide a topological as well as a computability-theoretic classification, which reveal some significant differences.

翻译：考虑到自然数字的可计算序列, 找到生成此序列的程序的 G\ “ odel 数 ” 是一个自然的任务。 很容易看到这个问题既不是连续的, 也不是可比较的。 在算法学习理论中,这个问题从几个角度得到了很好的研究, 并且研究了一个问题, 这个问题的顺序至少可以在极限中学习。 我们在这里研究所有可计算序列中的问题, 并分类其中的 Weishrauch 复杂性。 为此, 我们除其他方法外, 可以使用学习理论中已知的合并技术。 作为分类的基准, 我们使用封闭和紧凑的选择问题及其自然数字的跳跃, 我们争辩说, 这些问题符合感应和约束性原则, 正如人们所知道的基尔比- 巴黎等级的反数学。 我们提供了一种表学和可比较性理论分类, 揭示了一些重大差异。