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Concepts7

Groups

📐Linear Algebra15📈Calculus & Differentiation10🎯Optimization14🎲Probability Theory12📊Statistics for ML9📡Information Theory10🔺Convex Optimization7🔢Numerical Methods6🕸Graph Theory for Deep Learning6🔵Topology for ML5🌐Differential Geometry6∞Measure Theory & Functional Analysis6🎰Random Matrix Theory5🌊Fourier Analysis & Signal Processing9🎰Sampling & Monte Carlo Methods10🧠Deep Learning Theory12🛡️Regularization Theory11👁️Attention & Transformer Theory10🎨Generative Model Theory11🔮Representation Learning10🎮Reinforcement Learning Mathematics9🔄Variational Methods8📉Loss Functions & Objectives10⏱️Sequence & Temporal Models8💎Geometric Deep Learning8

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🔷All∑Math⚙️Algo🗂️DS📚Theory

Level

AllBeginnerIntermediate
⚙️AlgorithmAdvanced

Interior Point Methods

Interior point methods solve constrained optimization by replacing hard constraints with a smooth barrier that becomes infinite at the boundary, keeping iterates strictly inside the feasible region.

#interior point method#logarithmic barrier#central path+12
⚙️AlgorithmAdvanced

ADMM (Alternating Direction Method of Multipliers)

ADMM splits a hard optimization problem into two easier subproblems that communicate through simple averaging-like steps.

#admm
Advanced
Group:
Convex Optimization
#alternating direction method of multipliers
#augmented lagrangian
+11
⚙️AlgorithmIntermediate

Proximal Operators & Methods

A proximal operator pulls a point x toward minimizing a function f while penalizing how far it moves, acting like a denoiser or projector depending on f.

#proximal operator#ista#fista+12
∑MathAdvanced

KKT Conditions

KKT conditions generalize Lagrange multipliers to handle inequality constraints in constrained optimization problems.

#kkt conditions#lagrangian#complementary slackness+12
∑MathIntermediate

Convex Optimization Problems

A convex optimization problem minimizes a convex function over a convex set, guaranteeing that every local minimum is a global minimum.

#convex optimization#gradient descent#projected gradient+12
∑MathIntermediate

Convex Sets & Functions

A set is convex if every line segment between any two of its points lies entirely inside the set.

#convex set#convex function#convex hull+11
📚TheoryIntermediate

Lagrangian Duality

Lagrangian duality turns a constrained minimization problem into a related maximization problem that provides lower bounds on the original objective.

#lagrangian duality#kkt conditions#slater condition+11