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From Exercise 88, F, = $I,/&, where ( = ( 1 + & ) / 2 = 1618 ConI sequently, an AVL tree of height H has at least (roughly) @H+'/& nodes Hence its depth is at most logarithmic The height of an AVL tree satisfies

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5 When you click the Finish button, the typed data set definition will be added to your project as an XML Schema Definition (XSD) file with an associated typed data set definition source file that is hidden by default You can view the actual typed data set code by expanding the Solution Explorer tree underneath the XSD file Underneath the XSD file is a Designercs file for the data set that contains the autogenerated class definitions for the data set and its associated table adapters Additionally, the Data Sources window will update to show the objects in your data set

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When designing a submit-only form, you might wander back into the Data Connections or Submit Options dialogs If you care to modify the Main submit data connection (the one created when you rst designed your form template), you will end up stepping through the Data Connection Wizard again However, this time, you will encounter the wizard page that asks you to select parameter mappings This page s settings will be empty, and you will be required to set the mapping at this time Read on to the next section to learn how to set the mappings properly

so the worst-case height is at most roughly 44 percent more than the minimum possible for binary trees The depth of an average node in a randomly constructed AVL tree tends to be very close to log N The exact answer has not yet been established analytically We do not even known whether the form is log N + C or ( 1 + E) log N + C, for some E that would be approximately 001 Simulations have been unable to demonstrate convincingly that one form is more plausible than the other A consequence of these arguments is that all searching operations in an AVL tree have logarithmic worst-case bounds The difficulty is that operations that change the tree, such as insert and remove,are not quite as simple as before The reason is that an insertion (or deletion) can destroy the balance of several nodes in the tree, as shown in Figure 1921 The balance must then be restored before the operation can be considered complete The insertion algorithm is described here, and the deletion algorithm is left for Exercise 1911 A key observation is that after an insertion, only nodes that are on the path from the insertion point to the root might have their balances altered because only those nodes have their subtrees altered This result applies to almost all the balanced search tree algorithms As we follow the path up to the root and update the balancing information, we may find a node whose

An update in an AVL tree could destroy the balance It must then be rebalanced before the operation can be considered complete

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