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Meet the concept↗ The four techniques are greedy strategies, LP relaxation and rounding, the primal-dual schema, and local search.
Conditions: Applies to the design of approximation algorithms for NP-hard problems.
The four techniques are greedy strategies, LP relaxation and rounding, the primal-dual schema, and local search.
Conditions: Applies to the design of approximation algorithms for NP-hard problems.
Meet the concept↗ FPTAS and PTAS allow arbitrarily close approximation. APX allows constant-factor approximation.
Conditions: Comparing standard approximation classes for optimization problems.
FPTAS and PTAS allow arbitrarily close approximation. APX allows constant-factor approximation.
Conditions: Comparing standard approximation classes for optimization problems.
Understand why↗ The algorithm selects a set of disjoint edges M. The optimal cover must include at least one vertex per edge in M, so optimal cost >= |M|.
Conditions: The algorithm selects arbitrary edges and adds both endpoints until no edges remain
The algorithm selects a set of disjoint edges M. The optimal cover must include at least one vertex per edge in M, so optimal cost >= |M|.
Conditions: The algorithm selects arbitrary edges and adds both endpoints until no edges remain