Skip to main navigation
Skip to search
Skip to main content
Teesside University's Research Portal Home
Search content at Teesside University's Research Portal
Home
Profiles
Research units
TeesRep
Student theses
Projects
Datasets
Equipment
Press/Media
Utilitarian Mechanism Design for Multiobjective Optimization
Fabrizio Grandoni
, Piotr Krysta
, Stefano Leonardi
, Carmine Ventre
School of Computing, Engineering & Digital Technologies
Teesside University
Research output
:
Contribution to journal
›
Article
›
peer-review
219
Downloads (Pure)
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Utilitarian Mechanism Design for Multiobjective Optimization'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Utilitarian
100%
Mechanism Design
100%
Optimization Problem
100%
Budget Constraint
100%
Multi-objective Optimization Problem
100%
Multi-objective Optimization
100%
Monotone
66%
Approximate Pareto Set
66%
Feasible Solution
66%
Lagrangian Relaxation
66%
FPTAS
66%
Minimum Spanning Tree
66%
World Wide Web
33%
Private Knowledge
33%
Selfish Agents
33%
Algorithmic Mechanism Design
33%
Polynomial Time
33%
Independence System
33%
Multidimensional Knapsack
33%
Minimum Cost Spanning Tree
33%
Multi-criteria
33%
Approximation Ratio
33%
Dominant Strategy
33%
Multi-unit Combinatorial Auctions
33%
Perfect Matching
33%
Las Vegas
33%
Integral Length
33%
Random Perturbation
33%
Approximate Solution
33%
Approximation Scheme
33%
Shortest Path
33%
Expected Running Time
33%
NP-hard Problem
33%
Matroid Intersection
33%
Approximation Algorithms
33%
Heavy Elements
33%
Optimum Solution
33%
Pseudo-polynomial Time Algorithm
33%
Goal Conflict
33%
Natural Settings
33%
Computer Science
Multiobjective
100%
Optimization Problem
100%
Mechanism Design
100%
Multi-Objective Optimization
66%
Budget Constraint
50%
Lagrangian Relaxation
33%
Feasible Solution
33%
Fully Polynomial-Time Approximation Scheme
33%
Minimum Spanning Tree
33%
Relaxation Method
16%
Combinatorial Auction
16%
Approximate Solution
16%
Approximation Algorithms
16%
polynomial-time algorithm
16%
Perfect Matchings
16%
Objective Function
16%
Dominant Strategy
16%
Polynomial Time
16%
Approximation Ratio
16%
Computed Solution
16%
Random Perturbation
16%
Good Approximation
16%
Knapsack
16%
Optimum Solution
16%
approximation scheme
16%
Private Knowledge
16%
Minimum Cost Spanning Tree
16%
Mathematics
Matroid
33%