Convex optimization finance

  • Is convex optimization used in finance?

    Convex analysis allows for many generalizations to some fundamental results in mathematical finance.
    Convex optimization provides computational possibilities beyond the techniques of stochastic analysis alone. 1.Aug 21, 2012.

  • What is convex optimization in finance?

    Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets (or, equivalently, maximizing concave functions over convex sets)..

Aug 21, 2012Convex analysis allows for many generalizations to some fundamental results in mathematical finance. • Convex optimization provides 
Convex analysis allows for many generalizations to some fundamental results in mathematical finance. Convex optimization provides computational possibilities beyond the techniques of stochastic analysis alone.
Convex optimization problems have helpful mathematical properties — namely, that any local minimum must be a global minimum. This avoids suboptimal solutions. The constraints in portfolio optimization, like budget constraints, limits on asset holdings, etc., can often be formulated as linear constraints.
Portfolio optimization is a classic example of convex optimization in finance. The goal is to find the optimal allocation of assets that maximizes the return while minimizing the risk. The objective function is usually a quadratic function that represents the portfolio's risk and returns, and the constraints are typically linear.

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