Linked from

The 24 pages that link to Convex optimization, each with the reason it gives.

• Least squaresNarrower topic: A squared-error objective is convex, making global minimization tractable.

• Convex setRelated: Convex feasible sets help ensure that local optima are global optima.

• Banach spaceRelated: Banach spaces provide settings for optimization over functions and other infinite-dimensional variables.

• Calculus of variationsCompared with: Convexity can guarantee global optima, whereas variational stationarity alone does not.

• Normed vector spaceRelated: Norms often define objectives and constraints, including regularization penalties.

• Convex functionRelated: Every local minimum of a convex objective over a convex domain is globally optimal.

• Nonlinear systemCompared with: Convexity can make nonlinear optimization tractable even when equations are not linear.

• Combinatorial optimizationCompared with: Convex structure often permits efficient global optimization unlike general discrete search.

• Constrained optimizationCompared with: Convexity can turn local optimality conditions into global certificates.

• Convex geometryRelated: Convex sets make local optima global under standard conditions.

• ConvexityNarrower topic: Convexity ensures that every local minimum is global.

• Convex combinationNarrower topic: Convex combinations preserve feasible regions and support optimization methods.

• Karush–Kuhn–Tucker conditionsNarrower topic: Under suitable convexity and regularity, KKT conditions can certify global optimality.

• Legendre transformNarrower topic: Convex conjugates turn constraints and sums into forms useful for deriving dual optimization problems.

• Mathematical optimizationBroader topic: Convex structure makes global solutions and efficient algorithms more accessible.

• Convex analysisBroader topic: It is the principal problem class where convex analysis supplies global guarantees.

• Interior-point methodNarrower topic: The strongest general guarantees for interior-point algorithms are established in convex optimization.

• Optimization problemBroader topic: Its structure makes local optima global under standard formulations.

• Support functionRelated: Support functions express constraints and appear in dual optimization formulations.

• Maximum and minimumBroader topic: Convexity can make a local minimum a global minimum.

• Nonlinear programmingCompared with: Convexity supplies global-optimality guarantees absent from general nonlinear programming.

• Sylvester's criterionRelated: Positive-definite Hessians establish strict convexity in many optimization problems.

• Ellipsoid methodNarrower topic: The ellipsoid method applies to convex problems through their feasible regions and separating information.

• Hyperplane separation theoremNarrower topic: Separating hyperplanes underpin optimality and infeasibility arguments in convex programs.