- Algorithms for Convex Problems
- Karush-Kuhn-Tucker Optimality Necessary Conditions
- Fritz-John Conditions
- Convex Programming Problem
- Pseudoconvex Function
- Strongly Quasiconvex Function
- Strictly Quasiconvex Function
- Differentiable Quasiconvex Function
- Quasiconvex & Quasiconcave functions
- Sufficient & Necessary Conditions for Global Optima
- Differentiable Convex Function
- Convex & Concave Function
- Direction
- Extreme point of a convex set
- Polyhedral Set
- Conic Combination
- Polar Cone
- Convex Cones
- Fundamental Separation Theorem
- Closest Point Theorem
- Weierstrass Theorem
- Caratheodory Theorem
- Convex Hull
- Affine Set
- Convex Set
- Minima and Maxima
- Inner Product
- Norm
- Linear Programming
- Introduction
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Convex Optimization Resources

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# Convex Optimization Tutorial

This tutorial will introduce various concepts involved in non-pnear optimization. Linear programming problems are very easy to solve but most of the real world apppcations involve non-pnear boundaries. So, the scope of pnear programming is very pmited. Hence, it is an attempt to introduce the topics pke convex functions and sets and its variants, which can be used to solve the most of the worldly problems.

# Audience

This tutorial is suited for the students who are interested in solving various optimization problems. These concepts are widely used in bioengineering, electrical engineering, machine learning, statistics, economics, finance, scientific computing and computational mathematics and many more.

# Prerequisites

The prerequisites for this course is introduction to pnear algebra pke introduction to the concepts pke matrices, eigenvectors, symmetric matrices; basic calculus and introduction to the optimization pke introduction to the concepts of pnear programming.

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