The properties handled are: polynomial (or linear) boundedness of computed values, as a function of the input; and similarly for the running time. It is well known that complexity properties are undecidable for a Turing-complete programming language. Much work in program analysis overcomes this ...
Redbean segments each read into 256 bp tiling subsequences, reducing the dynamic programming matrix to a size of 65536 ( = 256 × 256), thus speeding up the pairwise alignment process19. On the other hand, to address the high error rate of long reads, Shasta compresses all ...
Linear programming is a method for solving complex, real-life business problems, using the power of mathematics. Organizations have been applying this method for 50+ years, across nearly all industries, to optimize operational efficiency—to get the most value from their limited resources. For examp...
The method is very fast in practice, although it has an exponential worst-case time complexity. Polynomial-time algorithms for linear programs were presented by Khachiyan [36] and Karmarkar [33, 32]. For further reading on linear programming we recommend Chvatal [14] or Nemhauser and Wolsey [...
There are several suitable and well-known Python tools for linear programming and mixed-integer linear programming. Some of them are open source, while others are proprietary. Whether you need a free or paid tool depends on the size and complexity of your problem as well as on the need for...
摘要原文 The complexity of linear programming is discussed in the “integer” and “real number” models of computation. Even though the integer model is widely used in theoretical computer science, the real number model is more useful for estimating an algorithm's running time in actual computati...
The chapter discusses the complexity of linear programming and two new methods鈥攖he ellipsoid method and Karmarkar's projective method. These are distinguished from the simplex method and have the desirable theoretical property of polynomial-time boundedness....
Seidel's LP Algorithm: Linear-Complexity Linear Programming (LP) for Small-Dimensions About This solver is super efficient for small-dimensional LP with any constraint number, mostly encountered in computational geometry. It enjoys linear complexity about the constraint number. The speed is at least ...
The Sort Transform (ST) can significantly speed up the block sorting phase of the Burrows-Wheeler transform (BWT) by sorting only limited order contexts. However, the best result obtained so far for the inverse ST has a time complexity O(Nlogk) and a spa
SDQP: Small-Dimensional Strictly Convex Quadratic Programming in Linear Time About This solver is super efficient for small-dimensional strictly convex QP with any constraint number, mostly encountered in computational geometry. It enjoyslinear complexity about the constraint number. ...