RL Researcher

Convex sets affine and convex sets some important examples operations that preserve convexity generalized inequalities separating and supporting hyperplanes dual cones and generalized inequalities Affine set 아래에 그림의 $x_{1}$과 $x_{2}$를 통하는 모든 점들을 Affine set이라고 부름. $$x = \theta x_{1}+(1-\theta)x_{2}\ \ \ \ \ (\theta \in R)$$ affine set : set에서 두 개의 다른 점을 통과하는 선을 포함함. example : linear equations $\le..

Question 1 The symbol x⋆ usually denotes a feasible point the optimal value of the problem a solution Question 2 Least squares is a special case of convex optimization. true false Question 3 Almost any problem you'd like to solve in practice is convex. true false Question 4 Convex optimization problems are attractive because they always have a unique solution. true false Question 5 In device siz..

1.1 Mathematical optimization 수학적 최적화 문제, 또는 그냥 최적화 문제는 다음과 같은 형식을 가집니다. vector $x=(x_{1},...,x_{n})$ called optimization variable(최적화 변수). function $f_0 : R^{n}\rightarrow R$ called objective function(목적함수). function $f_{i} : R^{n}\rightarrow R, \ \ i=1,...,m$ called (inequality)constraint functions(부등식)제약함수. constant $b_{1},...,b_{m}$ constraints에 대한 limits or bounds. vector $x^{*}$은 called so..

Introduction mathematical optimization (수학적 최적화) least-squares and linear programming (최소 제곱과 선형 계획법) convex optimization (볼록 최적화) example (예시) course goals and topics (코스 목표 및 주제) nonlinear optimization (비선형 최적화) brief history of convex optimization (볼록 최적화의 간략한 역사) Mathematical Optimization Optimization Problem의 정의 : $x = (x_{1}, ..., x_{n})$ : Optimization variables(최적화 변수), Decision variable..