Newbetuts

find the maximum $\frac{\frac{x^2_{1}}{x_{2}}+\frac{x^2_{2}}{x_{3}}+\cdots+\frac{x^2_{n-1}}{x_{n}}+\frac{x^2_{n}}{x_{1}}}{x_{1}+x_{2}+\cdots+x_{n}}$

Intuition Behind Accelerated First Order Methods

L1 norm and L2 norm

Open Problems in Convex Analysis and Convex Optimization

Can $X - Y A^\dagger Y^T\succ0$ be written as an LMI where $A^\dagger$ is a pseudoinverse?

Are greedy methods such as orthogonal matching pursuit considered obsolete for finding sparse solutions?

Projection onto a convex

Proof of convexity of linear least squares

Constraint qualification for linear constraints [closed]

invex functions and their usefulness?

How is the subdifferential of the $l_2$ norm at $x=0$ the polar of the unit ball?

Why the unit circle in $\mathbf{R^2}$ has one dimension?

Gradient is NOT the direction that points to the minimum or maximum

Pointwise supremum of a convex function collection

Dual problem of a quadratic problem

What's wrong with solving the least-squares problem here? (Simple question)

Prove optimal solution to dual is not unique if optimal solution to the primal is degenerate and unique.

New posts in convex-optimization

Writing a convex quadratic program (QP) as a semidefinite program (SDP)

Convex Optimization Problem (example)

What exactly is non-convex optimization

find the maximum $\frac{\frac{x^2_{1}}{x_{2}}+\frac{x^2_{2}}{x_{3}}+\cdots+\frac{x^2_{n-1}}{x_{n}}+\frac{x^2_{n}}{x_{1}}}{x_{1}+x_{2}+\cdots+x_{n}}$

Intuition Behind Accelerated First Order Methods

L1 norm and L2 norm

Open Problems in Convex Analysis and Convex Optimization

Can $X - Y A^\dagger Y^T\succ0$ be written as an LMI where $A^\dagger$ is a pseudoinverse?

Are greedy methods such as orthogonal matching pursuit considered obsolete for finding sparse solutions?

Projection onto a convex

Proof of convexity of linear least squares

Constraint qualification for linear constraints [closed]

invex functions and their usefulness?

How is the subdifferential of the $l_2$ norm at $x=0$ the polar of the unit ball?

Why the unit circle in $\mathbf{R^2}$ has one dimension?

Gradient is NOT the direction that points to the minimum or maximum

Pointwise supremum of a convex function collection

Dual problem of a quadratic problem

What's wrong with solving the least-squares problem here? (Simple question)

Prove optimal solution to dual is not unique if optimal solution to the primal is degenerate and unique.