Learning Importance of Preferences

10.29007/v68w ◽  
2018 ◽  
Author(s):  
Ying Zhu ◽  
Mirek Truszczynski
Keyword(s):  
Polynomial Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Scoring Rules ◽  
Np Hard ◽  
Individual Preferences

We study the problem of learning the importance of preferences in preference profiles in two important cases: when individual preferences are aggregated by the ranked Pareto rule, and when they are aggregated by positional scoring rules. For the ranked Pareto rule, we provide a polynomial-time algorithm that finds a ranking of preferences such that the ranked profile correctly decides all the examples, whenever such a ranking exists. We also show that the problem to learn a ranking maximizing the number of correctly decided examples (also under the ranked Pareto rule) is NP-hard. We obtain similar results for the case of weighted profiles when positional scoring rules are used for aggregation.

A POLYNOMIAL-TIME ALGORITHM FOR COMPUTING THE RESILIENCE OF ARRANGEMENTS OF RAY SENSORS

2014 ◽  
Vol 24 (03) ◽  
pp. 225-236 ◽  
Author(s):  
DAVID KIRKPATRICK ◽  
BOTING YANG ◽  
SANDRA ZILLES
Keyword(s):  
Polynomial Time ◽  
Linear Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
The Other ◽  
Np Hard ◽  
Sensor Coverage ◽  
Entire Plane ◽  
Line Segments ◽  
Minimum Number

Given an arrangement A of n sensors and two points s and t in the plane, the barrier resilience of A with respect to s and t is the minimum number of sensors whose removal permits a path from s to t such that the path does not intersect the coverage region of any sensor in A. When the surveillance domain is the entire plane and sensor coverage regions are unit line segments, even with restricted orientations, the problem of determining the barrier resilience is known to be NP-hard. On the other hand, if sensor coverage regions are arbitrary lines, the problem has a trivial linear time solution. In this paper, we study the case where each sensor coverage region is an arbitrary ray, and give an O(n2m) time algorithm for computing the barrier resilience when there are m ⩾ 1 sensor intersections.

scholarly journals On the Max-Min Fair Stochastic Allocation of Indivisible Goods

2020 ◽  
Vol 34 (02) ◽  
pp. 2070-2078
Author(s):  
Yasushi Kawase ◽  
Hanna Sumita
Keyword(s):  
Approximate Solution ◽  
Approximation Algorithm ◽  
Polynomial Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Np Hard ◽  
Indivisible Goods ◽  
Additive Utility ◽  
Ex Ante ◽  
Stochastic Setting

We study the problem of fairly allocating a set of indivisible goods to risk-neutral agents in a stochastic setting. We propose an (approximation) algorithm to find a stochastic allocation that maximizes the minimum utility among the agents. The algorithm runs by repeatedly finding an (approximate) allocation to maximize the total virtual utility of the agents. This implies that the problem is solvable in polynomial time when the utilities are gross-substitutes (which is a subclass of submodular). When the utilities are submodular, we can find a (1 − 1/e)-approximate solution for the problem and this is best possible unless P=NP. We also extend the problem where a stochastic allocation must satisfy the (ex ante) envy-freeness. Under this condition, we demonstrate that the problem is NP-hard even when every agent has an additive utility with a matroid constraint (which is a subclass of gross-substitutes). Furthermore, we propose a polynomial-time algorithm for the setting with a restriction that the matroid constraint is common to all agents.

scholarly journals On the complexity of the balanced vertex ordering problem

2007 ◽  
Vol Vol. 9 no. 1 (Graph and Algorithms) ◽  
Author(s):  
Jan Kára ◽  
Jan Kratochvil ◽  
David R. Wood
Keyword(s):  
Polynomial Time ◽  
Maximum Degree ◽  
Graph Drawing ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Np Hard ◽  
Vertex Ordering ◽  
The Difference ◽  
Balanced Ordering ◽  
Applied Math

Graphs and Algorithms International audience We consider the problem of finding a balanced ordering of the vertices of a graph. More precisely, we want to minimise the sum, taken over all vertices v, of the difference between the number of neighbours to the left and right of v. This problem, which has applications in graph drawing, was recently introduced by Biedl et al. [Discrete Applied Math. 148:27―48, 2005]. They proved that the problem is solvable in polynomial time for graphs with maximum degree three, but NP-hard for graphs with maximum degree six. One of our main results is to close the gap in these results, by proving NP-hardness for graphs with maximum degree four. Furthermore, we prove that the problem remains NP-hard for planar graphs with maximum degree four and for 5-regular graphs. On the other hand, we introduce a polynomial time algorithm that determines whetherthere is a vertex ordering with total imbalance smaller than a fixed constant, and a polynomial time algorithm that determines whether a given multigraph with even degrees has an 'almost balanced' ordering.

Benchmark of Bitrate Adaptation in Video Streaming

2021 ◽  
Vol 13 (4) ◽  
pp. 1-24
Author(s):  
Jessica Chen ◽  
Henry Milner ◽  
Ion Stoica ◽  
Jibin Zhan
Keyword(s):  
Dynamic Programming ◽  
Polynomial Time ◽  
Computation Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Perceptual Quality ◽  
Programming Approach ◽  
Np Hard ◽  
Http Adaptive Streaming ◽  
The Future

The HTTP adaptive streaming technique opened the door to cope with the fluctuating network conditions during the streaming process by dynamically adjusting the volume of the future chunks to be downloaded. The bitrate selection in this adjustment inevitably involves the task of predicting the future throughput of a video session, owing to which various heuristic solutions have been explored. The ultimate goal of the present work is to explore the theoretical upper bounds of the QoE that any ABR algorithm can possibly reach, therefore providing an essential step to benchmarking the performance evaluation of ABR algorithms. In our setting, the QoE is defined in terms of a linear combination of the average perceptual quality and the buffering ratio. The optimization problem is proven to be NP-hard when the perceptual quality is defined by chunk size and conditions are given under which the problem becomes polynomially solvable. Enriched by a global lower bound, a pseudo-polynomial time algorithm along the dynamic programming approach is presented. When the minimum buffering is given higher priority over higher perceptual quality, the problem is shown to be also NP-hard, and the above algorithm is simplified and enhanced by a sequence of lower bounds on the completion time of chunk downloading, which, according to our experiment, brings a 36.0% performance improvement in terms of computation time. To handle large amounts of data more efficiently, a polynomial-time algorithm is also introduced to approximate the optimal values when minimum buffering is prioritized. Besides its performance guarantee, this algorithm is shown to reach 99.938% close to the optimal results, while taking only 0.024% of the computation time compared to the exact algorithm in dynamic programming.

ALIGNMENT-TO-ALIGNMENT EDITING WITH “MOVE GAP” OPERATIONS

1996 ◽  
Vol 07 (01) ◽  
pp. 23-41
Author(s):  
MARTIN FÜRER ◽  
WEBB MILLER
Keyword(s):  
Polynomial Time ◽  
Dna Sequences ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Np Hard ◽  
Natural Class ◽  
Run Length

An alignment of k given sequences is a k-rowed matrix frequently used by molecular biologists to display correspondences between entries from each sequence. Under one approach, an alignment is represented by a matrix of ‘x’ and ’-’ characters, where each x in row r indicates the position of an entry of sequence r. It is sometimes efficient to store only the run-length encoding of each row of this bit-matrix. A natural class of commands for editing one such row into another consists of operations of the form: “Move the d dashes that begin at position i of row r to position j of that row,” for relevant values of r, d, i and j. We show that the problem of determining a shortest sequence of such operations that converts one given alignment to another is NP-hard and give a polynomial-time algorithm that always comes within a factor 5/4 of optimality. An application of these ideas to alignments of long DNA sequences is discussed.

The Deletion-Insertion model applied to the genome rearrangement problem

2019 ◽  
Vol 28 (1) ◽  
pp. 1-13
Author(s):  
Abra Brisbin ◽  
Manda Riehl ◽  
Noah Williams
Keyword(s):  
Polynomial Time ◽  
Genome Rearrangement ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Small Scale ◽  
Permutation Statistics ◽  
Np Hard ◽  
Minimum Number ◽  
Linear Chromosomes

Abstract Permutations are frequently used in solving the genome rearrangement problem, whose goal is finding the shortest sequence of mutations transforming one genome into another. We introduce the Deletion-Insertion model (DI) to model small-scale mutations in species with linear chromosomes, such as humans. Applying one restriction to this model, we obtain the transposition model for genome rearrangement, which was shown to be NP-hard in [4]. We use combinatorial reasoning and permutation statistics to develop a polynomial-time algorithm to approximate the minimum number of transpositions required in the transposition model and to analyze the sharpness of several bounds on transpositions between genomes.

ANALYZING VULNERABILITIES OF CRITICAL INFRASTRUCTURES USING FLOWS AND CRITICAL VERTICES IN AND/OR GRAPHS

2004 ◽  
Vol 15 (01) ◽  
pp. 107-125 ◽  
Author(s):  
YVO DESMEDT ◽  
YONGGE WANG
Keyword(s):  
Problem Solving ◽  
Polynomial Time ◽  
Model Problem ◽  
Minimum Cost ◽  
Maximum Flow ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Np Hard ◽  
Time Period ◽  
Concurrent Processes

AND/OR graphs and minimum-cost solution graphs have been studied extensively in artificial intelligence (see, e.g., Nilsson [14]). Generally, the AND/OR graphs are used to model problem solving processes. The minimum-cost solution graph can be used to attack the problem with the least resource. However, in many cases we want to solve the problem within the shortest time period and we assume that we have as many concurrent resources as we need to run all concurrent processes. In this paper, we will study this problem and present an algorithm for finding the minimum-time-cost solution graph in an AND/OR graph. We will also study the following problems which often appear in industry when using AND/OR graphs to model manufacturing processes or to model problem solving processes: finding maximum (additive and non-additive) flows and critical vertices in an AND/OR graph. A detailed study of these problems provide insight into the vulnerability of complex systems such as cyber-infrastructures and energy infrastructures (these infrastructures could be modeled with AND/OR graphs). For an infrastructure modeled by an AND/OR graph, the protection of critical vertices should have highest priority since terrorists could defeat the whole infrastructure with the least effort by destroying these critical points. Though there are well known polynomial time algorithms for the corresponding problems in the traditional graph theory, we will show that generally it is NP-hard to find a non-additive maximum flow in an AND/OR graph, and it is both NP-hard and coNP-hard to find a set of critical vertices in an AND/OR graph. We will also present a polynomial time algorithm for finding a maximum additive flow in an AND/OR graph, and discuss the relative complexity of these problems.

scholarly journals Destroying Bicolored $P_3$s by Deleting Few Edges

2021 ◽  
Vol vol. 23 no. 1 (Graph Theory) ◽  
Author(s):  
Niels Grüttemeier ◽  
Christian Komusiewicz ◽  
Jannik Schestag ◽  
Frank Sommer
Keyword(s):  
Polynomial Time ◽  
Maximum Degree ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Np Hard ◽  
Induced Subgraph ◽  
Bounded Degree ◽  
Red Edge ◽  
Bounded Degree Graphs

We introduce and study the Bicolored $P_3$ Deletion problem defined as follows. The input is a graph $G=(V,E)$ where the edge set $E$ is partitioned into a set $E_r$ of red edges and a set $E_b$ of blue edges. The question is whether we can delete at most $k$ edges such that $G$ does not contain a bicolored $P_3$ as an induced subgraph. Here, a bicolored $P_3$ is a path on three vertices with one blue and one red edge. We show that Bicolored $P_3$ Deletion is NP-hard and cannot be solved in $2^{o(|V|+|E|)}$ time on bounded-degree graphs if the ETH is true. Then, we show that Bicolored $P_3$ Deletion is polynomial-time solvable when $G$ does not contain a bicolored $K_3$, that is, a triangle with edges of both colors. Moreover, we provide a polynomial-time algorithm for the case that $G$ contains no blue $P_3$, red $P_3$, blue $K_3$, and red $K_3$. Finally, we show that Bicolored $P_3$ Deletion can be solved in $ O(1.84^k\cdot |V| \cdot |E|)$ time and that it admits a kernel with $ O(k\Delta\min(k,\Delta))$ vertices, where $\Delta$ is the maximum degree of $G$. Comment: 25 pages

General polynomial decomposition and the s-1-decomposition are NP-Hard

1993 ◽  
Vol 04 (02) ◽  
pp. 147-156 ◽  
Author(s):  
Matthew T. Dickerson
Keyword(s):  
Polynomial Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Np Hard ◽  
Decomposition Problem ◽  
The Past ◽  
Polynomial Decomposition ◽  
General Polynomial ◽  
In The Wild ◽  
Univariate Polynomial

In the past few years, much work has been done on the functional decomposition of polynomials. Beginning with the first polynomial time algorithm of Kozen and Landau1 for the decomposition of a univariate polynomial in the “tame” case, significant progress has been made toward polynomial time algoithms for the more general cases: decomposition of multivariate polynomials, and decomposition in the “wild” case.2−8 However it has remained an open problem whether general multivariate decomposition is in P. In this paper, we present a basic form for the general polynomial decomposition problem which encompasses most forms of previously examined decomposition problems, and then prove that the problem is NP-Hard by proving that a sub-problem called the S-1-Decomposition problem is NP-Hard.

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