منابع مشابه
Combinatorial Optimization and Integer Linear Programming Combinatorial Optimization
Examples: Shortest Path, Traveling Salesman, and many many more. . . Just in bioinformatics: Alignments, Threading, Clone-Probe Mapping, Probe Selection, De Novo Peptide Sequencing, SideChain Placement, Maximum-weight Connected Subgraph in PPI Networks, Genome Rearrangements, Cluster Editing, Finding Regulatory Modules, Finding Approximate Gene Clusters, Haplotyping, and many more. . . Example....
متن کاملCombinatorial Optimization and Integer Linear Programming
Examples: Shortest Path, Traveling Salesman, and many many more. . . Just in bioinformatics: Alignments, Threading, Clone-Probe Mapping, Probe Selection, De Novo Peptide Sequencing, SideChain Placement, Maximum-weight Connected Subgraph in PPI Networks, Genome Rearrangements, Cluster Editing, Finding Regulatory Modules, Finding Approximate Gene Clusters, Haplotyping, and many more. . . Example....
متن کاملCSC 2411 - Linear Programming and Combinatorial Optimization ∗ Lecture 5 : Smoothed Analysis , Randomized Combinatorial Algorithms , and Linear Programming Duality
In this class, we discuss a few " post-simplex-algorithm " issues. We will first study the smoothed case analysis of Linear Programming problems. We then learn the Seidel's algorithm, a randomized com-binatorial algorithm that run in subexponential time, and its extensions. Last, we will be introduced to the duality theorem of Linear Programs. 1 Overview In the previous lecture, we learned the ...
متن کاملCombinatorial Linear Programming: Geometry Can Help
We consider a class A of generalized linear programs on the d-cube (due to Matoušek) and prove that Kalai’s subexponential simplex algorithm Random-Facet is polynomial on all actual linear programs in the class. In contrast, the subexponential analysis is known to be best possible for general instances in A. Thus, we identify a “geometric” property of linear programming that goes beyond all abs...
متن کاملCSC 2411 - Linear Programming and Combinatorial Optimization ∗ Lecture 9 : Semi - Definite Programming Combinatorial Optimization
This lecture consists of two main parts. In the first one, we revisit Semi-Definite Programming (SDP). We show its equivalence to Vector Programming, we prove it has efficient membership and separation oracles and finally state a theorem that shows why Ellipsoid can be used to acquire an approximate solution of a semi-definite program. In the second part, we make a first approach to Combinatori...
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ژورنال
عنوان ژورنال: Computers & Mathematics with Applications
سال: 1978
ISSN: 0898-1221
DOI: 10.1016/0898-1221(78)90040-8