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Topics For Matlab Simulink :
- Least-squares,linear programming,Nonlinear optimization, gradient descent, Newton’s method, CVX,convex analysis and geometry,Affine and convex sets ,functions, subdifferential calculus, Linear and quadratic programming
- Semidefinite programming, conic optimization,Optimality conditions, duality theory,theorems of alternative, Interior-point methods, augmented Lagrangians, alternating direction method of multipliers, Applications in machine learning
- Convex regularization, compressed sensing ,matrix completion,Geometric programming,Generalized inequality constraints,Vector optimization, Lagrange dual function,Geometric interpretation,Saddle-point interpretation
- Optimality conditions,Perturbation and sensitivity analysis,Parametric distribution estimation, Nonparametric distribution estimation,Optimal detector design and hypothesis testing, Chebyshev and Chernoff bounds
Few Topics are:
- Convex sets and functions;
- convex optimization problems;
- geometric and Lagrangian duality;
- simplex algorithm;
- ellipsoid algorithm;
- matroid theory;
- submodular optimization
Complex Topics are:
- convex analysis
- convex sets, functions and optimization problems
- optimization theory
- linear, quadratic, semidefinite and geometric programming
- optimality conditions and duality theory
- optimization algorithms
- descent methods and interior-point methods
- signal processing, control
- communications, networks
- statistics, machine learning
- circuit design and mechanical engineering
- distributed decomposition
- exact convex relaxation
- parsimonious recovery
Some of the topics are:
- convex analysis
- Convex sets, functions
- optimization problems
- Optimization theory
- Least-squares, linear, quadratic, geometric and semidefinite programming
- Convex modeling
- Duality theory
- Optimality and KKT conditions
- signal processing, statistics, machine learning
- control communications, and design of engineering systems
