Explain the necessary and sufficient conditions in case of unconstrained optimisation

In the context of unconstrained optimization, the necessary and sufficient conditions refer to the criteria that must be satisfied for a point to be a minimum, maximum, or a saddle point of a given function.

These conditions are crucial for identifying critical points and determining whether they correspond to local minima, maxima, or points of inflection. The most commonly used conditions are the first and second-order conditions.

First-Order Necessary Condition:

The first-order necessary condition is based on the derivative of the function. For a function ( f(x) ), a critical point occurs where the derivative is equal to zero or undefined. Mathematically, this condition is expressed as:

[ f'(x_0) = 0 ]

This condition helps identify potential points where the function may have a minimum, maximum, or a saddle point.

Second-Order Necessary Condition:

The second-order necessary condition involves the second derivative of the function. The second derivative provides information about the concavity of the function at a critical point. For a function ( f(x) ), if ( f”(x_0) > 0 ), it indicates that the function is concave upward at that point, suggesting a local minimum. If ( f”(x_0) < 0 ), it indicates a concave downward shape, suggesting a local maximum. Mathematically:

[ f”(x_0) > 0 \quad \text{(Concave upward, potential local minimum)} ]
[ f”(x_0) < 0 \quad \text{(Concave downward, potential local maximum)} ]

Second-Order Sufficient Condition:

The second-order sufficient condition provides further information to confirm whether a critical point is indeed a minimum, maximum, or a saddle point. For a minimum, the second derivative must be positive, and for a maximum, it must be negative. Mathematically:

[ f”(x_0) > 0 \quad \text{(Minimum)} ]
[ f”(x_0) < 0 \quad \text{(Maximum)} ]


  1. First-Order Necessary Condition:
  • ( f'(x_0) = 0 ) (Identifies potential critical points)
  1. Second-Order Necessary Condition:
  • ( f”(x_0) > 0 ) (Concave upward, potential local minimum)
  • ( f”(x_0) < 0 ) (Concave downward, potential local maximum)
  1. Second-Order Sufficient Condition:
  • ( f”(x_0) > 0 ) (Minimum)
  • ( f”(x_0) < 0 ) (Maximum)

It’s important to note that while these conditions are necessary or sufficient for local extrema, they do not guarantee the global nature of the extremum. Additionally, the conditions are derived assuming smooth functions, and care should be taken in the presence of non-differentiable points or discontinuities.

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