L.P. All rights reserved.© 2024 Bloomberg Finance L.P. All rights reserved. Designing a Slimmer Vector of Variants CppCon 2024 September 18, 2024 Chris Fretz Senior C++ Engineer 2© 2024 Bloomberg details my experience in creating a novel solution to an observed problem with memory usage of std::vector> • The talk starts with the motivating use case, considers several candidate Heterogeneous containers like std::vector> are an extremely natural way to represent data across many different paradigms • Naive usage of std::vector> can result in

1#include #include #include <vector> int a[] = {0, 1, 2, 3, 4}; std::vector c = {0, 1, 2, 3, 4}; auto main() -> int { std::cout << "C style array: " << sizeof(a) / sizeof(a[0]) sizeof(a[0]) << std::endl; std::cout << "Vector size: " << c.size() << std::endl; return 0; } 1 2 3 4 5 6 7 8 9 10 11 12 13 14 https://www.reddit.com/r/cpp/comments/qm6i25/my_professor_is_telling _us_to_never_use_vectors/ 2more std::vector? 3How do you read a technical book front to back? As a technical editor... 4#include int main() { int cppconEarlyYears[6] = {2014, 2015, 2016

Lecture Notes on Support Vector Machine Feng Li fli@sdu.edu.cn Shandong University, China 1 Hyperplane and Margin In a n-dimensional space, a hyper plane is defined by ωT x + b = 0 (1) where ω ∈ Rn Rn is the outward pointing normal vector, and b is the bias term. The n-dimensional space is separated into two half-spaces H+ = {x ∈ Rn | ωT x + b ≥ 0} and H− = {x ∈ Rn | ωT x + b < 0} by the hyperplane margin is defined as γ = min i γ(i) (6) 1 ? ? ! ? ! Figure 1: Margin and hyperplane. 2 Support Vector Machine 2.1 Formulation The hyperplane actually serves as a decision boundary to differentiating

Lecture 6: Support Vector Machine Feng Li Shandong University fli@sdu.edu.cn December 28, 2021 Feng Li (SDU) SVM December 28, 2021 1 / 82 Outline 1 SVM: A Primal Form 2 Convex Optimization Review Hyperplane Separates a n-dimensional space into two half-spaces Defined by an outward pointing normal vector ω ∈ Rn Assumption: The hyperplane passes through origin. If not, have a bias term b; we will then along ω (b < 0 means in opposite direction) Feng Li (SDU) SVM December 28, 2021 3 / 82 Support Vector Machine A hyperplane based linear classifier defined by ω and b Prediction rule: y = sign(ωTx +

Reserved. zip_vector template 
 class zip_vector { vector _first;
 vector _second; public: const vector& first() const { return _first; }
 const vector& second() Reserved. zip_vector template 
 class zip_vector { vector _first;
 vector _second; public: const vector& first() const { return _first; }
 const vector& second() Reserved. zip_vector template 
 class zip_vector { vector _first;
 vector _second; public: const vector& first() const { return _first; }
 const vector& second()