diff --git a/src/optimize/projected/projectednewton.jl b/src/optimize/projected/projectednewton.jl
index 89a5269..949afe6 100644
--- a/src/optimize/projected/projectednewton.jl
+++ b/src/optimize/projected/projectednewton.jl
@@ -61,16 +61,33 @@ end
 isbinding(i, j) = i & j
 
 factorize(ab::ActiveBox, M) = ab.factorize(M)
-
 function solve(
-    prob::OptimizationProblem,
+    problem::OptimizationProblem,
     x0,
+    approach::ActiveBox,
+    options::OptimizationOptions,
+)
+    B0 = false * x0 * x0' + I
+    s0 = (x0, B0)
+    _solve(problem, s0, approach, options)
+end
+function solve(
+    problem::OptimizationProblem,
+    s0::Tuple,
+    approach::ActiveBox,
+    options::OptimizationOptions,
+)
+    _solve(problem, s0, approach, options)
+end
+
+function _solve(
+    prob::OptimizationProblem,
+    s0::Tuple,
     scheme::ActiveBox,
     options::OptimizationOptions,
 )
     t0 = time()
-
-    x0, B0 = x0, (false * x0 * x0' + I)
+    x0, B0 = s0
 
     lower, upper = bounds(prob)
     if isnothing(scheme.ϵ)
diff --git a/src/optimize/univariate/brentmin.jl b/src/optimize/univariate/brentmin.jl
index c8d2488..3d2d0b2 100644
--- a/src/optimize/univariate/brentmin.jl
+++ b/src/optimize/univariate/brentmin.jl
@@ -14,6 +14,7 @@ function solve(
 end
 
 function _solve(prob, bm::BrentMin, options)
+    time0 = time()
     a, b = bounds(prob)
     T = typeof(a)
     t = 1e-8
@@ -22,10 +23,11 @@ function _solve(prob, bm::BrentMin, options)
 
     e = d = 0 * x
 
-    fv = fw = fx = value(prob, x)
+    f0 = fv = fw = fx = value(prob, x)
     p = q = r = 0 * x
-
-    for i = 1:options.maxiter
+    brent_iter = 0
+    while brent_iter < options.maxiter
+        brent_iter += 1
         m = (a + b) / 2
         tol = eps(T) * abs(x) + t
         if abs(x - m) > 2 * tol - (b - a) / 2 # stopping crit
@@ -45,15 +47,11 @@ function _solve(prob, bm::BrentMin, options)
             end
 
             if abs(p) < abs(q * r / 2) && p < q * (a - x) && p < q * (b - x)
-                # the do the parapolic interpolation
+                # then do the parapolic interpolation
                 d = p / q
                 u = x + d
                 if u - a < 2 * tol || b - u < 2 * tol
-                    if x < m # this should just use sign
-                        d = tol
-                    else
-                        d = -tol
-                    end
+                    d = sign(m-x)*tol
                 end
             else
                 # do golden section
@@ -104,12 +102,6 @@ function _solve(prob, bm::BrentMin, options)
             break
         end
     end
-    return x, fx
-end
-
 
-#=
-prob = OptimizationProblem(ScalarObjective(;f=x->sign(x)), (-2.0,2.0))
-@time solve(prob, 0.0, BrentMin(), OptimizationOptions())
-
-=#
+    return ConvergenceInfo(bm, (;x, f0, minimum=fx, time=time()-time0, iter=brent_iter), options)
+end
diff --git a/test/optimize/interface.jl b/test/optimize/interface.jl
index c7ccd0d..9a0cf99 100644
--- a/test/optimize/interface.jl
+++ b/test/optimize/interface.jl
@@ -250,12 +250,21 @@ const brent_prob = OptimizationProblem(brent_scalar, (-2.0, 2.0))
     @test alloc == 0
 end
 
+const f3(x) = abs(x)
+const obj3 = ScalarObjective(; f=f3)
+const prob3 = OptimizationProblem(obj3, (-10.1, 9.0))
 @testset "brentmin" begin
     f(x) = (5.0 + x)^2.0
     obj = ScalarObjective(; f)
     prob = OptimizationProblem(obj, (-10.1, 9.0))
 
-    @test all(abs.(solve(prob, BrentMin(), OptimizationOptions()) .- (-5.0, 0.0)) .< 1e-8)
+    f2(x) = abs(x)
+    obj2 = ScalarObjective(; f=f2)
+    prob2 = OptimizationProblem(obj2, (-10.1, 9.0))
+
+    @test all(abs.(solve(prob, BrentMin(), OptimizationOptions()).info.minimum) .< 1e-8)
+    @test all(abs.(solve(prob2, BrentMin(), OptimizationOptions()).info.minimum) .< 1e-8)
+    @test all(abs.(solve(prob3, BrentMin(), OptimizationOptions()).info.minimum) .< 1e-8)
 end
 
 
@@ -523,6 +532,11 @@ solve(static_prob_qn, rand(3), AdaMax(), OptimizationOptions(maxiter = 1000))
     @test res_unc.info.solution ≈ [3.0, 2.0]
     res_con = solve(prob_on_bounds, copy(start), ActiveBox(), OptimizationOptions())
     @test res_con.info.solution ≈ [3.5, 1.6165968467448326]
+
+    res_con_matrix = solve(prob_on_bounds, (copy(start), [1.0 0.0;0.0 1.0]) , ActiveBox(), OptimizationOptions())
+    @test res_con_matrix.info.B isa Matrix
+    res_con_mmatrix = solve(prob_on_bounds, (copy(start), @MMatrix([1.0 0.0;0.0 1.0])) , ActiveBox(), OptimizationOptions())
+    @test res_con_mmatrix.info.B isa MMatrix
 end
 
 function fourth_f(x)