Advanced Data Structures by Peter Brass

By Peter Brass

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Advanced facts buildings offers a finished examine the information, research, and implementation information of information constructions as a really good subject in utilized algorithms. facts buildings are how facts is kept inside of a working laptop or computer, and the way you'll be able to move approximately looking for info inside of. this article examines effective how you can seek and replace units of numbers, durations, or strings via numerous information buildings, resembling seek bushes, buildings for units of periods or piece-wise consistent features, orthogonal variety seek buildings, tons, union-find constructions, dynamization and endurance of buildings, constructions for strings, and hash tables. this can be the 1st quantity to teach facts constructions as a vital algorithmic subject, instead of relegating them as trivial fabric used to demonstrate object-oriented programming technique, filling a void within the ever-increasing desktop technological know-how marketplace. quite a few code examples in C and greater than 500 references make complex info buildings an quintessential textual content. subject. a variety of code examples in C and greater than 500 references make complex information buildings an necessary text.
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Alt. ISBN:0521880378, 0521880378, 9780521880374

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There might be other references to this object. But if this is the only reference to the object, this will cause a memory leak, so we should delete the object. This is the responsibility of the user, so we return a pointer to the object. 5 Returning from Leaf to Root Any tree operation starts at the root and then follows the path down to the leaf where the relevant object is or where some change is performed. In all the balanced search-tree versions we will discuss in Chapter 3, we need to return along this path, from the leaf to the root, to perform some update or 36 2 Search Trees rebalancing operations on the nodes of this path.

A stack: If we push pointers to all traversed nodes on a stack during descent to the leaf, then we can take the nodes from the stack in the correct (reversed) sequence afterward. This is the cleanest solution under the criterion of information economy; it does not put any additional information into the tree structure. Also, the maximum size of the stack needed is the height of the tree, and so for the balanced search trees, it is logarithmic in the size of the search tree. An array-based stack for 200 items is really enough for all realistic applications because we will never have 2100 items.

Stack_empty() ) { tr_node = pop(); if( tr_node->right == NULL ) { /* reached leaf, now test */ if( a <= tr_node->key && tr_node->key < b ) { tmp = get_node(); /* leaf key in interval */ tmp->key = tr_node->key; /* copy to output list */ tmp->left = tr_node->left; tmp->right = result_list; result_list = tmp; } } /* not leaf, might have to follow down */ else if ( b <= tr_node->key ) /* entire interval left */ push( tr_node->left ); else if ( tr_node->key <= a ) /* entire interval right */ push( tr_node->right ); else /* node key in interval, follow left and right */ { push( tr_node->left ); push( tr_node->right ); } } remove_stack(); return( result_list ); } Listing the keys in an interval is a one-dimensional range query.

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