CS168: The Modern Algorithmic Toolbox Lecture #14: Linear and Convex Programming, with Applications to Sparse Recovery
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چکیده
Recall the setup in compressive sensing. There is an unknown signal z ∈ R, and we can only glean information about z through linear measurements. We choose m linear measurements a1, . . . , am ∈ R. “Nature” then chooses a signal z, and we receive the results b1 = 〈a1, z〉, . . . , bm = 〈am, z〉 of our measurements, when applied to z. The goal is then to recover z from b. Last lecture culminated in the following sparse recovery guarantee for compressive sensing.
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CS168: The Modern Algorithmic Toolbox Lecture #18: Linear and Convex Programming, with Applications to Sparse Recovery
Recall the setup in compressive sensing. There is an unknown signal z ∈ R, and we can only glean information about z through linear measurements. We choose m linear measurements a1, . . . , am ∈ R. “Nature” then chooses a signal z, and we receive the results b1 = 〈a1, z〉, . . . , bm = 〈am, z〉 of our measurements, when applied to z. The goal is then to recover z from b. Last lecture culminated i...
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