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This paper develops the linear-algebraic foundations of the Convex Least Squares Programming (CLSP) estimator and constructs its modular two-step convex optimization framework, capable of addressing ill-posed and underdetermined problems. After reformulating a problem in its canonical form, A(r)z(r)=b, Step~1 yields an iterated (if r>1) minimum-norm least-squares estimate z^(r)=(AZ(r))†b on a constrained subspace defined by a symmetric idempotent Z (reducing to the Moore-Penrose pseudoinverse when Z=I). The optional Step~2 corrects z^(r) by solving a convex program, which penalizes deviations using a Lasso/Ridge/Elastic net-based scheme parameterized by α∈[0,1] and yields z^∗. The second step guarantees a unique solution for α∈(0,1] and coincides with the Minimum-Norm BLUE (MNBLUE) when α=1. This paper also proposes an analysis of numerical stability and CLSP-specific goodness-of-fit statistics, such as partial R2, normalized RMSE (NRMSE), Monte Carlo t-tests for the mean of NRMSE, and condition-number-based confidence bands. The three special CLSP problem cases are then tested in a 50,000-iteration Monte Carlo experiment and on simulated numerical examples. The estimator has a wide range of applications, including interpolating input-output tables and structural matrices.