Complexity ( for Algorithm Designers ) Lecture # 8 : Lower Bounds in Algorithmic Game Theory ∗

اسناد مشابه

• CS 369 E : Communication Complexity ( for Algorithm Designers ) Lecture # 9 : Lower Bounds in Property Testing ∗

  سند علمی۲۰۱۵Tim Roughgarden

  We first give a brief introduction to the field of property testing. Section 3 gives upper bounds for the canonical property of “monotonicity testing,” and Section 4 shows how to derive property testing lower bounds from communication complexity lower bounds. We won’t need to dev

• CS 369 E : Communication Complexity ( for Algorithm Designers ) Lecture # 8 : Lower Bounds in Property Testing ∗

  سند علمی۲۰۱۵Tim Roughgarden

  We begin in this section with a brief introduction to the field of property testing. Section 2 explains the famous example of “linearity testing.” Section 3 gives upper bounds for the canonical problem of “monotonicity testing,” and Section 4 shows how to derive property testing

• CS 369 E : Communication Complexity ( for Algorithm Designers ) Lecture # 6 : Data Structure Lower Bounds ∗

  سند علمی۲۰۱۵Tim Roughgarden

  Next we discuss how to use communication complexity to prove lower bounds on the performance — meaning space, query time, and approximation — of data structures. Our case study will be the high-dimensional approximate nearest neighbor problem. There is a large literature on data

• CS369E: Communication Complexity (for Algorithm Designers) Lecture #3: Lower Bounds for Compressive Sensing∗

  سند علمی۲۰۱۵Tim Roughgarden

  We begin with an appetizer before starting the lecture proper — an example that demonstrates that randomized one-way communication protocols can sometimes exhibit surprising power. It won’t surprise you that the Equality function — with f(x,y) = 1 if and only if x = y — is a cent

• CS369E: Communication Complexity (for Algorithm Designers) Lecture #2: Lower Bounds for One-Way Communication: Disjointness, Index, and Gap-Hamming∗

  سند علمی۲۰۱۵Tim Roughgarden

  Recall from last lecture the simple but useful model of one-way communication complexity. Alice has an input x ∈ {0, 1}, Bob has an input y ∈ {0, 1}, and the goal is to compute a Boolean function f : {0, 1} × {0, 1} → {0, 1} of the joint input (x,y). The players communicate as in

• CS369E: Communication Complexity (for Algorithm Designers) Lectures #5–6: Lower Bounds for the Extension Complexity of Polytopes∗

  سند علمی۲۰۱۵Tim Roughgarden

  You’ve probably seen some polynomial-time algorithms for the problem of computing a maximum-weight matching of a bipartite graph. Many of these, like the Kuhn-Tucker algorithm [?] are “combinatorial algorithms,” meaning that all of its steps work directly with the graph. Linear p