id: 01910544
dt: j
an: 01910544
au: Dai, Yu-Hong
ti: A family of hybrid conjugate gradient methods for unconstrained
    optimization.
so: Math. Comput. 72, No.243, 1317-1328 (2003).
py: 2003
pu: American Mathematical Society, Providence, RI
la: EN
cc: 
ut: unconstrained optimization; conjugate gradient method; line search; descent
    property; global convergence
ci: 
li: doi:10.1090/S0025-5718-03-01491-1 
ab: Summary: Conjugate gradient methods are an important class of methods for
    unconstrained optimization, especially for large-scale problems.
    Recently, they have been much studied. This paper proposes a
    three-parameter family of hybrid conjugate gradient methods. Two
    important features of the family are that (i) it can avoid the
    propensity of small steps, namely, if a small step is generated away
    from the solution point, the next search direction will be close to the
    negative gradient direction; and (ii) its descent property and global
    convergence are likely to be achieved provided that the line search
    satisfies the Wolfe conditions. Some numerical results with the family
    are also presented.
rv: