An accelerated continuous greedy algorithm for maximizing strong submodular functions

2013 ◽  
Vol 30 (4) ◽  
pp. 1107-1124
Author(s):  
Zengfu Wang ◽  
Bill Moran ◽  
Xuezhi Wang ◽  
Quan Pan
Keyword(s):  
Greedy Algorithm ◽  
Submodular Functions

scholarly journals Regression under Human Assistance

2020 ◽  
Vol 34 (03) ◽  
pp. 2611-2620
Author(s):  
Abir De ◽  
Paramita Koley ◽  
Niloy Ganguly ◽  
Manuel Gomez-Rodriguez
Keyword(s):  
Machine Learning ◽  
Greedy Algorithm ◽  
Real World ◽  
Medical Diagnosis ◽  
Submodular Functions ◽  
Learning Models ◽  
Real World Data ◽  
Alternative Representation ◽  
The Greedy Algorithm ◽  
Machine Learning Models

Decisions are increasingly taken by both humans and machine learning models. However, machine learning models are currently trained for full automation—they are not aware that some of the decisions may still be taken by humans. In this paper, we take a first step towards the development of machine learning models that are optimized to operate under different automation levels. More specifically, we first introduce the problem of ridge regression under human assistance and show that it is NP-hard. Then, we derive an alternative representation of the corresponding objective function as a difference of nondecreasing submodular functions. Building on this representation, we further show that the objective is nondecreasing and satisfies α-submodularity, a recently introduced notion of approximate submodularity. These properties allow a simple and efficient greedy algorithm to enjoy approximation guarantees at solving the problem. Experiments on synthetic and real-world data from two important applications—medical diagnosis and content moderation—demonstrate that the greedy algorithm beats several competitive baselines.

scholarly journals Maximizing Sequence-Submodular Functions and Its Application to Online Advertising

2021 ◽  
Author(s):  
Saeed Alaei ◽  
Ali Makhdoumi ◽  
Azarakhsh Malekian
Keyword(s):  
Objective Function ◽  
Greedy Algorithm ◽  
Online Advertising ◽  
Optimal Solution ◽  
Allocation Problem ◽  
Submodular Functions ◽  
Query Rewriting ◽  
Nondecreasing Sequence

Motivated by applications in online advertising, we consider a class of maximization problems where the objective is a function of the sequence of actions and the running duration of each action. For these problems, we introduce the concepts of sequence-submodularity and sequence-monotonicity, which extend the notions of submodularity and monotonicity from functions defined over sets to functions defined over sequences. We establish that if the objective function is sequence-submodular and sequence-nondecreasing, then there exists a greedy algorithm that achieves [Formula: see text] of the optimal solution. We apply our algorithm and analysis to two applications in online advertising: online ad allocation and query rewriting. We first show that both problems can be formulated as maximizing nondecreasing sequence-submodular functions. We then apply our framework to these two problems, leading to simple greedy approaches with guaranteed performances. In particular, for the online ad allocation problem, the performance of our algorithm is [Formula: see text], which matches the best known existing performance, and for the query rewriting problem, the performance of our algorithm is [Formula: see text], which improves on the best known existing performance in the literature. This paper was accepted by Chung Piaw Teo, optimization.

A fast double greedy algorithm for non-monotone DR-submodular function maximization

2019 ◽  
Vol 12 (01) ◽  
pp. 2050007 ◽  
Author(s):  
Shuyang Gu ◽  
Ganquan Shi ◽  
Weili Wu ◽  
Changhong Lu
Keyword(s):  
Machine Learning ◽  
Greedy Algorithm ◽  
Submodular Function ◽  
Integer Lattice ◽  
Submodular Functions ◽  
Running Time ◽  
Submodular Function Maximization ◽  
Multiple Copies ◽  
Submodular Set Function ◽  
Set Function

We study the problem of maximizing non-monotone diminish return (DR)-submodular function on the bounded integer lattice, which is a generalization of submodular set function. DR-submodular functions consider the case that we can choose multiple copies for each element in the ground set. This generalization has many applications in machine learning. In this paper, we propose a [Formula: see text]-approximation algorithm with a running time of [Formula: see text], where [Formula: see text] is the size of the ground set, [Formula: see text] is the upper bound of integer lattice. Discovering important properties of DR-submodular function, we propose a fast double greedy algorithm which improves the running time.

Implementasi Algoritma Dijkstra Pada Game Pacman

CCIT Journal ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 170-176
Author(s):  
Anggit Dwi Hartanto ◽  
Aji Surya Mandala ◽  
Dimas Rio P.L. ◽  
Sidiq Aminudin ◽  
Andika Yudirianto
Keyword(s):  
Artificial Intelligence ◽  
Greedy Algorithm ◽  
Dijkstra Algorithm ◽  
Testing Phase ◽  
A Value ◽  
Route Problem ◽  
Starting Point ◽  
The Greedy Algorithm

Pacman is one of the labyrinth-shaped games where this game has used artificial intelligence, artificial intelligence is composed of several algorithms that are inserted in the program and Implementation of the dijkstra algorithm as a method of solving problems that is a minimum route problem on ghost pacman, where ghost plays a role chase player. The dijkstra algorithm uses a principle similar to the greedy algorithm where it starts from the first point and the next point is connected to get to the destination, how to compare numbers starting from the starting point and then see the next node if connected then matches one path with the path). From the results of the testing phase, it was found that the dijkstra algorithm is quite good at solving the minimum route solution to pursue the player, namely by getting a value of 13 according to manual calculations

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