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The Dai and Yuan conjugate gradient (CG) method is one of the classical CG algorithms using the numerator ‖gk+1‖2. When the usual Wolfe line search is used, the algorithm is shown to satisfy the descent condition and to converge globally when the Lipschitz condition is assumed. Despite these two advantages, the Dai-Yuan algorithm performs poorly numerically due to the jamming problem. This work will present an efficient variant of the Dai-Yuan CG algorithm that solves a nonlinear constrained monotone system (NCMS) and resolves the aforementioned problems. Our variant algorithm, like the unmodified version, converges globally when the Lipschitz condition and sufficient descent requirements are satisfied, regardless of the line search method used. Numerical computations utilizing algorithms from the literature show that this variant algorithm is numerically robust. Finally, the variant algorithm is used to reconstruct sparse signals in compressed sensing (CS) problems.