Randomization and Approximation Techniques in Computer Science

Polynomial time approximation schemes for some dense instances of NP-hard optimization problems.- Average-case complexity of shortest-paths problems in the vertex-potential model.- Approximation algorithms for covering polygons with squares and similar problems.- Greedily approximating the r-independent set and k-center problems on random instances.- Nearly linear time approximation schemes for Euclidean TSP and other geometric problems.- Random sampling of Euler tours.- A combinatorial consistency lemma with application to proving the PCP theorem.- Super-bits, demi-bits, and NP/qpoly-natural proofs.- Sample spaces with small bias on neighborhoods and error-correcting communication protocols.- Approximation on the web: A compendium of NP optimization problems.- Random-based scheduling new approximations and LP lower bounds.- ‘Go with the winners’ generators with applications to molecular modeling.- Probabilistic approximation of some NP optimization problems by finite-state machines.- Using hard problems to derandomize algorithms: An incomplete survey.- Weak and strong recognition by 2-way randomized automata.- Tally languages accepted by Monte Carlo pushdown automata.- Resource-bounded randomness and compressibility with respect to nonuniform measures.- Randomness, stochasticity and approximations.