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We show that $n$ real numbers can be stored in a constant number of real numbers such that each original real number can be fetched in $O(\log n)$ time. Although our result has implications for many computational geometry problems, we show here, combined with Han's $O(n\sqrt{\log n})$ time real number sorting algorithm [3, arXiv:1801.00776], we can improve the complexity of Kirkpatrick's point location algorithm [8] to $O(n\sqrt{\log n})$ preprocessing time, a constant number of real numbers for storage and $O(\log n)$ point location time. Kirkpatrick's algorithm uses $O(n\log n)$ preprocessing time, $O(n)$ storage and $O(\log n)$ point location time. The complexity results in Kirkpatrick's algorithm was the previous best result. Although Lipton and Tarjan's algorithm [10] predates Kirkpatrick's algorithm and has the same complexity, Kirkpatrick's algorithm is simpler and has a better structure. This paper can be viewed as a companion paper of paper [3, arXiv:1801.00776]., Comment: This is an interesting result, especially when read together with paper [3]

In a containment problem, the goal is to preprocess a set of geometric objects so that, given a geometric query object, we can report all the objects containing the query object. We consider the containment problem where input objects are homothetic triangles and the query objects considered are line segments, circles, and trapezoids with bases parallel to either axis. We show that this problem can be solved using the 3-d query dominance problem. The solutions presented can also be extended for higher dimensions.

In this paper, we investigate the homothetic point enclosure problem: given a set $S$ of $n$ triangles with sides parallel to three fixed directions, find a data structure for $S$ that can report all the triangles of $S$ that contain a query point efficiently. The problem is "inverse" of the homothetic range search problem. We present an $O(n\log n)$ space solution that supports the queries in $O(\log n + k)$ time, where $k$ is the output size. The preprocessing time is $O(n\log n)$. The same results also hold for homothetic polygons.

In this paper, we investigate the following 1-d range reporting problem: preprocess an array A[1: n] of n elements so that, given a pair of indices i, j (1

Consider the following game. We are given a tree $T$ and two players (say) Alice and Bob who alternately colour an edge of a tree (using one of $k$ colours). If all edges of the tree get coloured, then Alice wins else Bob wins. Game chromatic index of trees of is the smallest index $k$ for which there is a winning strategy for Alice. If the maximum degree of a node in tree is $��$, Erdos et.al.[6], show that the game chromatic index is at least $��+1$. The bound is known to be tight for all values of $��\neq 4$. In this paper we show that for $��=4$, even if Bob is allowed to skip a move, Alice can always choose an edge to colour and win the game for $k=��+1$. Thus the game chromatic index of trees of maximum degree $4$ is also $5$. Hence, game chromatic index of trees of maximum degree $��$ is $��+1$ for all $��\geq 2$. Moreover,the tree can be preprocessed to allow Alice to pick the next edge to colour in $O(1)$ time. A result of independent interest is a linear time algorithm for on-line edge-deletion problem on trees.

In this note, we present a simpler algorithm for joint seat allocation problem in case there are two or more merit lists. In case of two lists (the current situation for Engineering seats in India), the running time of the algorithm is proportional to sum of running time for two separate (delinked) allocations. The algorithm is straight forward and natural and is not (at least directly) based on deferred acceptance algorithm of Gale and Shapley. Each person can only move higher in his or her preference list. Thus, all steps of the algorithm can be made public. This will improve transparency and trust in the system.

In this paper, ellipsoid method for linear programming is derived using only minimal knowledge of algebra and matrices. Unfortunately, most authors first describe the algorithm, then later prove its correctness, which requires a good knowledge of linear algebra.

A caterpillar graph is a tree which on removal of all its pendant vertices leaves a chordless path. The chordless path is called the backbone of the graph. The edges from the backbone to the pendant vertices are called the hairs of the caterpillar graph. Ortiz and Villanueva (C.Ortiz and M.Villanueva, Discrete Applied Mathematics, 160(3): 259-266, 2012) describe an algorithm, linear in the size of the output, for finding a family of maximal independent sets in a caterpillar graph. In this paper, we propose an algorithm, again linear in the output size, for a generalised caterpillar graph, where at each vertex of the backbone, there can be any number of hairs of length one and at most one hair of length two.