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목록Optimization (12)
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..