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Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming
TLDR
This algorithm gives the first substantial progress in approximating MAX CUT in nearly twenty years, and represents the first use of semidefinite programming in the design of approximation algorithms.
A general approximation technique for constrained forest problems
TLDR
The first approximation algorithms for many NP-complete problems, including the non-fixed point-to-point connection problem, the exact path partitioning problem and complex location-design problems are derived.
The Design of Approximation Algorithms
TLDR
This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions to discrete optimization problems.
.879-approximation algorithms for MAX CUT and MAX 2SAT
TLDR
This research presents randomized approximation algorithms for the MAX CUT and MAX 2SAT problems that always deliver solutions of expected value at least .87856 times the optimal value and represents the first use of semidefinite programming in the design of approximation algorithms.
New 3/4-Approximation Algorithms for the Maximum Satisfiability Problem
TLDR
It is shown that although standard randomized rounding does not give a good approximate result, the best solution of the two given by randomized rounding and a well-known algorithm of Johnson is always within $\frac{3}{4}$ of the optimal solution.
The primal-dual method for approximation algorithms and its application to network design problems
TLDR
The primal-dual method was proposed by Dantzig, Ford, and Fulkerson [DFF56] as another means of solving linear programs, and Ironically, their inspiration came from combinatorial optimization.
Adversarial queueing theory
TLDR
A new approach to the study of dynamic (or continuous) packet routing, where packets are being continuously injected into a network, is introduced, based on the adversarial generation of packets, so that the results are more robust in that they do not hinge upon particular probabilistic assumptions.
Adversarial queuing theory
TLDR
An adversarial theory of queuing is developed aimed at addressing some of the restrictions inherent in probabilistic analysis and queuing theory based on time-invariant stochastic generation.
The Approximability of Constraint Satisfaction Problems
TLDR
Tight bounds on the "approximability" of every problem in Max Ones, Min CSP, and Min Ones are determined and this completely classifies all optimization problems derived from Boolean constraint satisfaction.
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