pessimistic estimator造句

• The pessimistic estimator is a function of the current state.
• The algorithm does not necessarily have to maximize ( or minimize ) the pessimistic estimator.
• The algorithm below chooses each vertex " u " to maximize the resulting pessimistic estimator.
• By the previous considerations, this keeps the pessimistic estimator from decreasing and guarantees a successful outcome.
• Typically, a good pessimistic estimator can be computed by precisely deconstructing the logic of the original proof.
• For the method of conditional probabilities to work, it suffices if the algorithm keeps the pessimistic estimator from decreasing ( or increasing, as appropriate ).
• In some cases, as a proxy for the exact conditional expectation of the quantity " Q ", one uses an appropriately tight bound called a " pessimistic estimator ".
• By calculation, if " u " is chosen randomly from the remaining vertices, the expected increase in the pessimistic estimator is non-negative . [ "'The calculation . "'
• When applying the method of conditional probabilities, the technical term "'pessimistic estimator "'refers to a quantity used in place of the true conditional probability ( or conditional expectation ) underlying the proof.
• Since the pessimistic estimator is a lower bound on the conditional expectation, this will ensure that the conditional expectation stays above | " V " | / ( " D " + 1 ), which in turn will ensure that the conditional probability of failure stays below 1.
• Young discovered the similarities between fast LP algorithms and Raghavan's method of pessimistic estimators for derandomization of randomized rounding algorithms; Klivans and Servedio linked boosting algorithms in learning theory to proofs of Yao's XOR Lemma; Garg and Khandekar defined a common framework for convex optimization problems that contains Garg-Konemann and Plotkin-Shmoys-Tardos as subcases.