This paper proposes new algorithms to solve canonical piecewise-linear equations with linear partitions and illustrates their efficiency through the analysis of resistive network. The basic idea of the proposed algorithm is to find the best next guess, closest to the actual solution, at each Newton-Raphson (N-R) iteration by comparing the images of nest guess candidates and that of the actual solution. The proposed algorithm can reduce the number of the N-R iterations rquired for convergence greatly, compared to the actual solution, at each Newton-Raphson (N-R) iteration by comparing the images of next guess candidates and that of the actual solution. The proposed algorithm can reduce the number of the N-R iterations required for convergence greatly, compared to the Katzenelson algorithm. When applied to analyzing test circuits, the proposed algorithm required 8 to 20 times fewer N-R iterations and 5 to 10 times less CPU time than the Katzenelson algorithm, depending on the size of the circuits. The experimental results also exhibit that the efficiency of the proposed algorithm over the Katzenelson algorithm increases as the number of the piecewise-linear regions for the representation of the circuit.