Skalierung von Daten
Formen der Suche auf Arrays
Welche Formen der Suche auf Arrays können wir unterscheiden?
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Konsequenzen der Skalierung
Welche Skalierungen von Daten unterscheiden wir im Kontext der Suche auf Arrays, und welche Konsequenzen ziehen sie jeweils für die Wahl des Suchverfahrens nach sich?
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Binäre Suche auf nominalen Daten
Warum ist eine binäre Suche auf nominal skalierten Daten prinzipiell unmöglich?
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Lineare und Binäre Suche
Komplexitätsvergleich
Vergleichen Sie die Laufzeitkomplexitäten (Worst Case, Average Case, Best Case) von linearer und binärer Suche hinsichtlich der Anzahl der Elementzugriffe.
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
Voraussetzungen für die binäre Suche
Nennen Sie die zwingenden Voraussetzungen, unter denen die binäre Suche korrekt arbeitet.
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Worst Case der binären Suche
Unter welchen Umständen tritt der Worst Case der binären Suche ein?
(Mögliche Erweiterung in einer Klausur: Geben Sie ein konkretes Beispiel für ein Array der Länge n=7 an.)
MTAwMDAw:eJ61PKBeJZv3m6bfENfidIivkykm2CWftn0iOC1jMsM=:vHZtvmnK2xPKVh0j: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
Grundidee und Annahmen
Ziel der Polynominterpolation
Was ist das Ziel der Polynominterpolation mittels Lagrange-Polynomen im Kontext der Suche auf Arrays?
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X/kQfvv3uQv9MhwZvBZkG9co+jgQcICPeqHf5wg8rFn1sgRgzzKMNRvZcVOwD9s48xRDGE7k99aXnCp9CRLWV+G2MkeiIACcJfuZk8lEFx3U3I5UcDwEVNDhJ3A2fLu/ulfHFdzuJQEf1BreAXx1/rnuE1vI7kflVKQFNs4GGbTiNwKUc8gVYMBHQhjZF0co7BXkqKXuQrPRe8uZaOxJONyrlRCyq/8vbwGdJbINIXDt+L0CbnmUc3AeTaeA9+bRBEO2c+JJQp9HUVJjS8QxmI9gvpgmkHh
Grad des Interpolationspolynoms
Wie hängt der Grad des Lagrange-Interpolationspolynoms von der Anzahl der gegebenen Stützpunkte ab?
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Wann ist Interpolation sinnvoll?
Unter welcher Annahme über die Verteilung der Daten ist eine interpolierende Suche der binären Suche überlegen? Wann kann sie schlechter sein?
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Korrektur der geschätzten Position
Im Algorithmus für die lineare interpolierende Suche wird die geschätzte Position pos mit folgender Zeile korrigiert:
pos := max(lower + 1, min(upper - 1, pos))
Warum ist diese Korrektur notwendig, obwohl die Daten sortiert sind?
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Anwendung und Laufzeit
Wann ist exponentielle Suche sinnvoll?
Nennen Sie zwei Situationen, in denen die exponentielle Suche der reinen binären Suche vorzuziehen ist, und begründen Sie dies.
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diHYdFXfRIQBX+jnZElE73rn3DAMV9dN7p5CIDkdvO43kXOvPJOIQk1ARx2j6jb2510qHzgMj/mMgxFG4q+NoPVTE/q/n1/WG0WXfzHdrfW8f4rVOCYOcbC3N8Y3DQcXAW1U/CudUi6SDwUBcMRWwCEzNscdGRD+WaGWSjHyBeIwW//suV7QapCcTpAShuP2+hvAnn7F81hkXzi6sFjxpULMbyp+3ZDXV0NwV8Wlnx++A==
Laufzeit der exponentiellen Suche
Wie lässt sich die Laufzeit der exponentiellen Suche asymptotisch ausdrücken, wenn sich der gesuchte Wert an Position i befindet?
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