scholarly journals Quit When You Can: Efficient Evaluation of Ensembles by Optimized Ordering

2021 ◽  
Vol 17 (4) ◽  
pp. 1-20
Author(s):  
Serena Wang ◽  
Maya Gupta ◽  
Seungil You
Keyword(s):  
Optimization Problem ◽  
Optimal Solution ◽  
Combinatorial Optimization Problem ◽  
Joint Optimization ◽  
Early Stopping ◽  
Time Approximation ◽  
Approximation Bound ◽  
Evaluation Time ◽  
Speed Up ◽  
Real World Problems

Given a classifier ensemble and a dataset, many examples may be confidently and accurately classified after only a subset of the base models in the ensemble is evaluated. Dynamically deciding to classify early can reduce both mean latency and CPU without harming the accuracy of the original ensemble. To achieve such gains, we propose jointly optimizing the evaluation order of the base models and early-stopping thresholds. Our proposed objective is a combinatorial optimization problem, but we provide a greedy algorithm that achieves a 4-approximation of the optimal solution under certain assumptions, which is also the best achievable polynomial-time approximation bound. Experiments on benchmark and real-world problems show that the proposed Quit When You Can (QWYC) algorithm can speed up average evaluation time by 1.8–2.7 times on even jointly trained ensembles, which are more difficult to speed up than independently or sequentially trained ensembles. QWYC’s joint optimization of ordering and thresholds also performed better in experiments than previous fixed orderings, including gradient boosted trees’ ordering.

An Approximate Algorithm for Triangle TSP with a Four-Vertex-Three-Line Inequality

2015 ◽  
Vol 6 (1) ◽  
pp. 35-46 ◽  
Author(s):  
Yong Wang
Keyword(s):  
Time Complexity ◽  
Optimization Problem ◽  
Nearest Neighbor ◽  
Optimal Solution ◽  
Simple Algorithm ◽  
Exact Algorithms ◽  
Combinatorial Optimization Problem ◽  
Traveling Salesman ◽  
Approximate Algorithm ◽  
Nearest Neighbor Algorithm

Traveling salesman problem (TSP) is a classic combinatorial optimization problem. The time complexity of the exact algorithms is generally an exponential function of the scale of TSP. This work gives an approximate algorithm with a four-vertex-three-line inequality for the triangle TSP. The time complexity is O(n2) and it can generate an approximation less than 2 times of the optimal solution. The paper designs a simple algorithm with the inequality. The algorithm is compared with the double-nearest neighbor algorithm. The experimental results illustrate the algorithm find the better approximations than the double-nearest neighbor algorithm for most TSP instances.

The Research of Special Laboratory Timetable Algorithm and Solving Conflicting Method

2012 ◽  
Vol 220-223 ◽  
pp. 3064-3067 ◽  
Author(s):  
Jun En Guo ◽  
Hong Xia Zhang
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Combinatorial Optimization Problem ◽  
Laboratory Personnel ◽  
Timetable Problem

The laboratory timetable problem is an NP combinatorial optimization problem, and it is difficult to get the optimal solution. Under the traditional timetable algorithm, classes are arranged by week, and it is fixed and regular that when and where classes are arranged. Whereas under special laboratory timetable algorithm, teachers need book in advance, classes are arranged by term, and it is very flexible and random. So the traditional timetable algorithm cannot solve the problem of the laboratory timetable. In order to solve this problem, a special laboratory timetable algorithm and a solving conflicting method are presented in this paper. It has been proven that it is a better solution to the laboratory timetable problem, saves a lot of time for the laboratory personnel and is worth further promote the application.

A Cross-Entropy Method for Solving Passenger Flow Routing Problem

2013 ◽  
Vol 756-759 ◽  
pp. 3617-3621 ◽  
Author(s):  
Yang Jiang ◽  
Xing Chen Zhang ◽  
Tian Tian Gan
Keyword(s):  
Optimization Problem ◽  
Optimal Solution ◽  
Entropy Method ◽  
Combinatorial Optimization Problem ◽  
Cross Entropy ◽  
Flow Routing ◽  
Passenger Flow ◽  
Cross Entropy Method ◽  
Routing Design ◽  
The Cross

In this paper, a new design optimization method-cross entropy methods for passenger flow routing in passenger hubs is employed, in order to develop rational and efficient passenger flow routing program, which will help improving the passenger flow organization. According to the description and characteristics of the problem, we transform the problem into a combinatorial optimization problem, so that it is convenient to explore the best solution. The numerical example declares that the method given above can obtain the optimal solution under the condition of fixed demand. Results show that the cross entropy method is effective, and can be well applied in passenger flow routing design problem. Systematic studying the passenger terminal passenger flow routing design optimization techniques and methods and developing rational and efficient passenger flow routing program will help improving the organization of passenger flow. According to the description of the problem and the characteristics of itself, the paper transforms the problem into a combinatorial optimization problem, so that it is convenient to explore the best solution. The paper also employs the cross entropy method to solve the problems. Numerical example declares that the method given above can obtain the optimal solution under the condition of fixed demand. Results show that the cross entropy method is effective, and this method can be well applied in passenger flow routing design problem.

scholarly journals An Interactive Regret-Based Genetic Algorithm for Solving Multi-Objective Combinatorial Optimization Problems

2020 ◽  
Vol 34 (03) ◽  
pp. 2335-2342
Author(s):  
Nawal Benabbou ◽  
Cassandre Leroy ◽  
Thibaut Lust
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Computation Time ◽  
Preference Elicitation ◽  
Combinatorial Optimization Problem ◽  
New Approach ◽  
Multi Objective ◽  
Combinatorial Optimization Problems

We propose a new approach consisting in combining genetic algorithms and regret-based incremental preference elicitation for solving multi-objective combinatorial optimization problems with unknown preferences. For the purpose of elicitation, we assume that the decision maker's preferences can be represented by a parameterized scalarizing function but the parameters are initially not known. Instead, the parameter imprecision is progressively reduced by asking preference queries to the decision maker during the search to help identify the best solutions within a population. Our algorithm, called RIGA, can be applied to any multi-objective combinatorial optimization problem provided that the scalarizing function is linear in its parameters and that a (near-)optimal solution can be efficiently determined when preferences are known. Moreover, RIGA runs in polynomial time while asking no more than a polynomial number of queries. For the multi-objective traveling salesman problem, we provide numerical results showing its practical efficiency in terms of number of queries, computation time and gap to optimality.

scholarly journals A New Distributed Approximation Algorithm for the Maximum Weight Independent Set Problem

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Peng Du ◽  
Yuan Zhang
Keyword(s):  
Optimization Problem ◽  
Optimal Solution ◽  
Independent Set ◽  
Wireless Networking ◽  
Combinatorial Optimization Problem ◽  
Maximum Weight ◽  
Approximation Accuracy ◽  
Maximum Weight Independent Set ◽  
Convergence Performance ◽  
Distributed Approximation

Maximum weight independent set (MWIS) is a combinatorial optimization problem that naturally arises in many applications especially wireless networking. This paper studies distributed approximation algorithms for finding MWIS in a general graph. In the proposed algorithm, each node keeps exchanging messages with neighbors in which each message contains partial solutions of the MWIS optimization program. A parameterHis introduced to achieve different tradeoff between approximation accuracy and space complexity. Theoretical analysis shows that the proposed algorithm is guaranteed to converge to an approximate solution after finite iterations; specifically, the proposed algorithm is guaranteed to converge to the optimal solution withH=+∞. Simulation results confirm the effectiveness of the proposed distributed algorithm in terms of weight sum, message size, and convergence performance.

scholarly journals Comparison of Swarm Intelligence Algorithms for High Dimensional Optimization Problem

2018 ◽  
Vol 11 (1) ◽  
pp. 300 ◽  
Author(s):  
Samar Bashath ◽  
Amelia Ritahani Ismail
Keyword(s):  
Swarm Intelligence ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
High Dimensional ◽  
High Dimensions ◽  
Solution Accuracy ◽  
Dimensional Optimization ◽  
Real World Problems ◽  
Comprehensive Study

<p>High dimensional optimization considers being one of the most challenges that face the algorithms for finding an optimal solution for real-world problems. These problems have been appeared in diverse practical fields including business and industries. Within a huge number of algorithms, selecting one algorithm among others for solving the high dimensional optimization problem is not an easily accomplished task. This paper presents a comprehensive study of two swarm intelligence based algorithms: 1-particle swarm optimization (PSO), 2-cuckoo search (CS).The two algorithms are analyzed and compared for problems consisting of high dimensions in respect of solution accuracy, and runtime performance by various classes of benchmark functions.</p><p> </p>

scholarly journals A Study on Greedy Technique in Container Loading Problem and Knapsack Problem

2021 ◽  
pp. 414-420
Author(s):  
S. Sathyapriya ◽  
V. Arundhathi ◽  
K. Aiswarya ◽  
S. R. Aarthi ◽  
S. Vishnu
Keyword(s):  
Greedy Algorithm ◽  
Knapsack Problem ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Geometric Constraints ◽  
Container Loading ◽  
Loading Problem ◽  
The Greedy Algorithm ◽  
Container Loading Problem ◽  
Real World Problems

The main aim of the paper is to use application of greedy algorithm in container loading problem and Knapsack problem. Greedy method gives an optimal solution to the problem by considering the inputs one at a time, checking to see if it can be included in the set of values which give an optimal solution and then check if it is the feasible solution. The Greedy algorithm could be understood very well with a well-known problem referred to as container loading problem and Knapsack problem. The basic Container Loading Problem can be defined as the problem of placing a set of boxes into the container respecting the geometric constraints: the boxes cannot overlap and cannot exceed the dimensions of the container. The knapsack problem is in combinatorial optimization problem. It appears as a sub problem in many, more complex mathematical models of real world problems.

A new algorithm for finding CRM-model coefficients

2019 ◽  
Vol 5 (3) ◽  
pp. 164-185 ◽  
Author(s):  
Alexander D. Bekman ◽  
Sergey V. Stepanov ◽  
Alexander A. Ruchkin ◽  
Dmitry V. Zelenin
Keyword(s):  
Approximate Solution ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Complex Form ◽  
Approximate Solutions ◽  
The Other ◽  
Programming Algorithm ◽  
Stochastic Algorithms ◽  
Large Set ◽  
Flow Rates

The quantitative evaluation of producer and injector well interference based on well operation data (profiles of flow rates/injectivities and bottomhole/reservoir pressures) with the help of CRM (Capacitance-Resistive Models) is an optimization problem with large set of variables and constraints. The analytical solution cannot be found because of the complex form of the objective function for this problem. Attempts to find the solution with stochastic algorithms take unacceptable time and the result may be far from the optimal solution. Besides, the use of universal (commercial) optimizers hides the details of step by step solution from the user, for example&nbsp;— the ambiguity of the solution as the result of data inaccuracy.<br> The present article concerns two variants of CRM problem. The authors present a new algorithm of solving the problems with the help of “General Quadratic Programming Algorithm”. The main advantage of the new algorithm is the greater performance in comparison with the other known algorithms. Its other advantage is the possibility of an ambiguity analysis. This article studies the conditions which guarantee that the first variant of problem has a unique solution, which can be found with the presented algorithm. Another algorithm for finding the approximate solution for the second variant of the problem is also considered. The method of visualization of approximate solutions set is presented. The results of experiments comparing the new algorithm with some previously known are given.

Inverse version of the kth maximization combinatorial optimization problem

2018 ◽  
Vol 54(5) ◽  
pp. 72
Author(s):  
Quoc, H.D. ◽  
Kien, N.T. ◽  
Thuy, T.T.C. ◽  
Hai, L.H. ◽  
Thanh, V.N.
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem
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