A parallel approximation algorithm for positive linear programming
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Summary
A fast parallel approximation algorithm for the positive linear programming optimization problem, where the input constraint matrix and constraint vector consist entirely of positive entries, that runs in polylog time using a linear number of processors.
- Type
- article
- Published
- 1993-06-01
- Cited by
- 215
- References
- 9
- Access
- Open access
- OpenAlex
- https://openalex.org/W2063241141
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:7513907
Keywords
Citation, Computer science, Linear programming, Algorithm, Approximation algorithm
References
- Probabilistic construction of deterministic algorithms: Approximating packing integer programs
- A deterministic view of random sampling and its use in geometry
- On the hardness of approximating minimization problems
- On Parallel Prefix Computation
- Approximate max flow on small depth networks
- Fast approximation algorithms for fractional packing and covering problems
- Introduction to Parallel Algorithms
- Parallel merge sort
- Efficient NC algorithms for set cover with applications to learning and geometry
Cited by
- Work efficient parallel scheduling algorithms
- Space-efficient Simulations of Quantum Interactive Proofs
- Combinatorial Problems of Packing and Covering and Related Problems of Integer Linear Programming
- A rank-predicted pseudo-greedy approach to efficient text selection from large-scale corpus for maximum coverage of target units
- A Primal-Dual Parallel Approximation Technique Applied to Weighted Set and Vertex Covers
- On the Complexity of Succinct Zero-Sum Games
- Minimizing the total cost of network measurements in a distributed manner: a primal-dual approach
- Solving Packing Integer Programs via Randomized Rounding with Alterations
- Using Optimization to Obtain a Width-Independent, Parallel, Simpler, and Faster Positive SDP Solver
- Solving Linear Programs in MapReduce
- Distributed combinatorial optimization
- A Fast Distributed Algorithm for α-Fair Packing Problems
- A Novel, Simple Interpretation of Nesterov's Accelerated Method as a Combination of Gradient and Mirror Descent
- A Distributed Approximation Algorithm for Mixed Packing-Covering Linear Programs
- (De)randomized construction of small sample spaces in /spl Nscr//spl Cscr/
- Distributed storage allocations for neighborhood-based data access
- Potential Function Methods for Approximately Solving Linear Programming Problems: Theory and Practice
- Lagrangian relaxation based algorithms for convex programming problems
- Approximating Scheduling Unrelated Parallel Machines in Parallel
- Near Linear Time Approximation Schemes for Uncapacitated and Capacitated b-Matching Problems in Nonbipartite Graphs
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