Optimization:: | p4r4d0xb0x

📄️00. Optimization

// PROBLEMTOALGORITHM — 최적화 문제를 정의에서 알고리즘 선택까지 다시 읽을 수 있게 만든 지도

// BACKGROUND 1/6 — set, interval, vector, function, mapping, summation, norm을 같은 방식으로 읽기

// BACKGROUND 2/6 — derivative, partial derivative, gradient, Hessian, Taylor expansion

// BACKGROUND 3/6 — vector, matrix, inner product, linear independence, eigenvalue, positive definiteness, linear systems

// BACKGROUND 4/6 — approximation error, floating-point arithmetic, iterative method, convergence, condition number

// BACKGROUND 5/6 — random variable, distribution, expectation, variance, sampling, regression

// BACKGROUND 6/6 — input-output model, simulation, ODE/PDE, objective definition, constraint definition

// CORE 1/7 — decision variables, objective function, constraints, feasible region

// CORE 2/7 — gradient/Jacobian/Hessian, Taylor approximation, convexity, norm, inner product, orthogonality, matrix decomposition

// CORE 3/7 — first-order condition, second-order condition, positive definiteness

// CORE 4/7 — gradient descent, exact line search, backtracking, Armijo, Wolfe, momentum, Nesterov

// CORE 5/7 — Newton, quasi-Newton, DFP, BFGS

// CORE 6/7 — coordinate descent, conjugate gradient, preconditioning

// CORE 7/7 — DOE, Latin Hypercube Sampling, surrogate modeling, surrogate-based optimization