物流分析工具包。Facility location: Continuous minisum facility location, alternate location-allocation (ALA) procedure, discrete uncapacitated facility location
Vehicle routing: VRP, VRP with time windows, traveling salesman problem (TSP)
Networks: Shortest path, min cost network flow, minimum spanning tree problems
Geocoding: U.S. city or ZIP code to longitude and latitude, longitude and latitude to nearest city, Mercator projection plotting
Layout: Steepest descent pairwise interchange (SDPI) heuristic for QAP
Material handling: Equipment selection
General purpose: Linear programming using the revised simplex method, mixed-integer linear programming (MILP) branch and bound procedure
Data: U.S. cities with populations of at least 10,000, U.S. highway network (Oak Ridge National Highway Network), U.S. 3- and 5-digit ZIP codes
Implemented BFS, DFS and A*
To compile this project, use the following command:
g++ -o search main.cpp
Then you can run it:
./search
The input is loaded from a input file in.txt
Here is the format of the input file:
The first line of the input file shoud contain two chars indicate the source and destination city for breadth first and depth first algorithm.
The second line of input file shoud be an integer m indicate the number of connections for the map.
Following m lines describe the map, each line represents to one connection in this form: dist city1 city2, which means there is a connection between city1 and city2 with the distance dist.
The following input are for A*
The following line contains two chars indicate the source and destination city for A* algorithm.
Then there is an integer h indicate the number of heuristic.
The following h lines is in the form: city dist which means the straight-line distance from the city to B is dist.
This paper studies the problem of categorical data clustering,
especially for transactional data characterized by high
dimensionality and large volume. Starting from a heuristic method
of increasing the height-to-width ratio of the cluster histogram, we
develop a novel algorithm – CLOPE, which is very fast and
scalable, while being quite effective. We demonstrate the
performance of our algorithm on two real world
密碼學界牛人Victor Shoup用C++編寫數論類庫。
NTL is a high-performance, portable C++ library providing data structures and algorithms for arbitrary length integers for vectors, matrices, and polynomials over the integers and over finite fields and for arbitrary precision floating point arithmetic.
NTL provides high quality implementations of state-of-the-art algorithms for:
* arbitrary length integer arithmetic and arbitrary precision floating point arithmetic
* polynomial arithmetic over the integers and finite fields including basic arithmetic, polynomial factorization, irreducibility testing, computation of minimal polynomials, traces, norms, and more
* lattice basis reduction, including very robust and fast implementations of Schnorr-Euchner, block Korkin-Zolotarev reduction, and the new Schnorr-Horner pruning heuristic for block Korkin-Zolotarev
* basic linear algebra over the integers, finite fields, and arbitrary precision floating point numbers.
Solve the 8-puzzle problem using A * algorithme.
Input: Program reads start state and goal state and heuristic (N or S) from EightPuzzle.INP file.0 representing blank.
There are 2 heuristic:
1. N: Number of misplaced tiles
2. S: Sum of Manhattan distance of current location and target location.
Format: The first line write type of heuristic (N or S).
Next is the status of departing and landing status. Between 2 states of 1 line blank.
See examples EightPuzzle.INP
In this paper we present a classifier called bi-density twin support vector machines (BDTWSVMs) for data classification. In the training stage, BDTWSVMs first compute the relative density degrees for all training points using the intra-class graph whose weights are determined by a local scaling heuristic strategy, then optimize a pair of nonparallel hyperplanes through two smaller sized support vector machine (SVM)-typed problems. In the prediction stage, BDTWSVMs assign to the class label depending
on the kernel density degree-based distances from each test point to the two hyperplanes. BDTWSVMs not only inherit good properties from twin support vector machines (TWSVMs) but also give good description for data points. The experimental results on toy as well as publicly available datasets
indicate that BDTWSVMs compare favorably with classical SVMs and TWSVMs in terms of generalization
