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A Quasi-Newton method that maintains a symmetric curvature approximation for differentiable optimization. For a positive-definite symmetric matrix $H$ approximating inverse curvature, step $s$, gradient change $y$ and $\rho =1/(y^{\mathsf{T}}s)>0$, its inverse update isMultiplying by $y$ gives $H_{+}y=s$, the inverse form of the Quasi-Newton secant equation. The update is a symmetric matrix. For any nonzero $v$,Both terms are nonnegative, and both can vanish only if $s^{\mathsf{T}}v=0$ and then $v=0$. Thus positive definiteness is preserved when $y^{\mathsf{T}}s>0$, making $-H_{+}\nabla f$ a descent direction away from a stationary point. Full updates use quadratic storage in the dimension; the method does not require explicit Hessian matrix evaluations. Superlinear convergence requires additional local smoothness and line search assumptions.^[ref]

Set $p=x^{k+1}-x^k={\alpha }^k{s}^k$ and $y=\nabla f(x^{k+1})-\nabla f(x^k)$. The Quasi-Newton secant equation is $B^{k+1}p=y$. If $B^{k+1}$ is a positive-definite symmetric matrix and $p\ne 0$, it follows thatThe weak curvature part of the Wolfe conditions ensures precisely this necessary condition:The last product is positive because ${\alpha }^k>0$, ${\beta }_2-1<0$, and $s^k$ is a descent direction. This explains why a Quasi-Newton method often uses the Wolfe conditions rather than only the Armijo condition.

Gradient descent uses only the current gradient and a suitable step size; its additional storage is order $d$. It is attractive for very large $d$ or when low per-iteration cost matters most. For a smooth strongly convex function, standard step choices give linear convergence, with a rate sensitive to the condition number. Mere convexity gives a global-minimum interpretation but does not alone imply a linear rate; for nonconvex objectives the usual guarantee concerns stationary points.

BFGS uses successive gradients to update an approximation to inverse curvature, incorporating information that can correct poor scaling. Full BFGS uses order $d^2$ storage and update work. With suitable line searches and local smoothness and positive-curvature assumptions near a nondegenerate minimum, it has superlinear convergence. This often favours it in moderate dimensions when gradients are not extremely cheap relative to the extra work.

Neither gradient descent nor BFGS requires an explicitly evaluated Hessian matrix. Thus an expensive Hessian matrix is compatible with both, rather than a reason to reject BFGS. If an accurate Hessian matrix and its factorization are exceptionally cheap, Newton's method in optimization becomes an additional reasonable option. Convexity helps identify a stationary point as a global minimum; without it, both methods need suitable descent and curvature safeguards. The BFGS update learns curvature from the Quasi-Newton secant equation.^[ref]

A Quasi-Newton method replaces the Hessian matrix in Newton's method in optimization by a matrix $B_k$ learned from successive gradients. Solving $B_k{s}_k=-\nabla f(x_k)$ gives a descent direction whenever $B_k$ is a positive-definite symmetric matrix and the gradient is nonzero. After choosing a step size, one updates $B_k$ to satisfy the Quasi-Newton secant equation, rather than calculating every second derivative again.

For $p_k=x_{k+1}-x_k$ and $y_k=\nabla f(x_{k+1})-\nabla f(x_k)$, the Quasi-Newton secant equation requires $B_{k+1}{p}_k=y_k$. It reproduces the observed change of gradient along the latest step. If $B_{k+1}$ is a positive-definite symmetric matrix and $p_k\ne 0$, necessarily $p_k^T{y}_k=p_k^T{B}_{k+1}{p}_k>0$. A line search satisfying the weak Wolfe conditions ensures this: with $p_k={\alpha }_k{s}_k$,This necessary curvature condition is also sufficient for the existence of some positive-definite symmetric matrix mapping $p_k$ to $y_k$. In an orthonormal basis whose first vector is $p_k/\Vert p_k\Vert$, its first column must be $(a,b{)}^T=y_k/\Vert p_k\Vert$, with $a=p_k^T{y}_k/\Vert p_k{\Vert }^2>0$. The block matrix with first row $(a,b^T)$ and lower-right block $b{b}^T/a+I$ has a positive Schur complement, so is positive-definite.

For a continuously differentiable function, a descent direction $s$ and $0<{\beta }_1<{\beta }_2<1$, the Wolfe conditions require both the Armijo condition andThis second inequality is the weak curvature condition: the slope must have become less negative. The strong variant instead requires $|\nabla f(x+\alpha s{)}^{T}s|\le {\beta }_2|\nabla f(x{)}^{T}s|$. The weak version already guarantees a positive curvature pair for the Quasi-Newton secant equation.